Spectral shape prediction from MDCT coefficients

Abstract

To provide a method, decoder and program code for controlling a concealment method for lost acoustic frames.SOLUTION: A concealment method includes: decoding a first acoustic frame and a second acoustic frame of a received acoustic signal for obtaining modified discrete cosine transform (MDCT) coefficients; determining a first spectral shape value based on the MDCT coefficients decoded from the decoded first acoustic frame and a second spectral shape value based on the MDCT coefficients decoded from the decoded second acoustic frame; converting the determined spectral shape values and frame energies of the first and second acoustic frames into an FFT-based representation of spectral analysis; detecting a transient state based on the converted FFT representation; and selectively adjusting spectrum magnitude of a substitution frame spectrum in response to the detection of the transient state.SELECTED DRAWING: Figure 9

Description

[Technical field]

[0001] The present invention relates generally to a method for concealing lost audio frames associated with a received audio signal, and also to a decoder arranged to conceal lost audio frames associated with a received encoded audio signal. [Background technology]

[0002] The transmission of speech / audio over modern communication channels / networks is primarily performed in the digital domain using speech / audio codecs. This may involve taking an analog signal to obtain digital samples, and digitizing the analog signal using sampling and an analog-to-digital converter (ADC). These digital samples may be further grouped into frames that contain samples from consecutive periods of 10-40 ms depending on the application. These frames may then be processed using compression algorithms that reduce the number of bits that need to be transferred while still achieving the highest possible quality. The coded bit stream is then transferred as data packets over a digital network to a receiver, where the process is reversed. The data packets may first be decoded to re-form frames with digital samples, and then the re-formed frames may be input to a digital-to-analog converter (DAC) to re-form an approximation of the input analog signal at the receiver. Figure 1 provides an example of a block diagram of audio transmission using an audio encoder and decoder over a network, such as a digital network, using the techniques described above.

[0003] When data packets are transferred over a network, there may be data packets that can either be dropped by the network due to traffic load or dropped as a result of bit errors that invalidate the digital data and make it impossible to decode. When these events occur, the decoder needs to replace the output signal during the period when it is actually impossible to decode. This replacement process is typically called frame / packet loss concealment. FIG. 2 illustrates a block diagram of a decoder 200 including packet loss concealment. When a bad frame indicator (BFI) indicates a lost or corrupted frame, the PLC 202 can generate a signal to replace the lost / corrupted frame. Otherwise, i.e., when the BFI does not indicate a lost or corrupted frame, the received signal is decoded by the stream decoder 204. Frame erasures can be signaled to the decoder by setting a bad frame indicator variable for the current frame to active, i.e., BFI=1. The decoded or concealed frame is then input to the DAC 206 to output an analog signal. The frame / packet loss concealment can also be called an error concealment unit (ECU).

[0004] There are many ways to implement packet loss concealment in the decoder. Some examples are replacing the lost frames with silence and repeating the last frame (or decoding the last frame parameters). Other solutions try to replace the frame with the most likely continuation of the acoustic signal. For noise-like signals, one solution generates noise with a similar spectral structure. For tonal signals, one can first predict the characteristics (frequency, amplitude, and phase) of the current tone and use these parameters to generate a continuation of the tone at the corresponding temporal position of the lost frame.

[0005] An example of another embodiment of the ECU is the Phase ECU originally described in International Patent Application WO2014123470, where the decoder can continuously store a prototype of the decoded signal during normal decoding. This prototype can be used in case of a lost frame. The prototype has its spectrum analyzed and the noise and timbre ECU functions are combined in the spectral domain. The Phase ECU identifies the tones and calculates the spectral temporal replacement of the relevant spectral bins. The other bins can be treated as noise and are scrambled to avoid timbre artifacts in these spectral regions. The resulting reconstructed spectrum is inverse FFT (Fast Fourier Transform) transformed to the time domain and the signal is processed to create a replacement for the lost frame.

[0006] Most signals rarely last for long periods of time. To better handle error bursts, the ECU can use a low-resolution spectral prediction of the current input signal as a long-term background prediction. During longer bursts, the ECU can change the target to reconstruct over time. Initially, the target is to replace the last signal. Over time, the target can be switched to a low-resolution background prediction that will make the prediction more noise-like and eventually move to a target that mute the output.

[0007] For packet loss concealment, a low-resolution spectral representation of the current input is required. In the embodiments described in International Patent Application WO2014123471 (see Appendix 1), the spectral representation can be formed from two short FFTs, each of which is a quarter of a prototype frame, and is used for transient detection. For short transforms, the prediction may have high variance. To obtain a more stable long-term prediction, the average of the two FFTs can be used. In these embodiments, the FFT bins are grouped into sub-bands that approximately represent the critical bands in the hearing system.

[0008] The long-term prediction can be used as a target spectrum in longer error bursts, so that over time lost frames can be replaced with a signal representative of the current input background signal.

[0009] The drawback of this approach is that the short FFT substantially increases the complexity in the first error frame, which already has a high complexity since it is used to perform the complete PLC chain of spectral analysis, sinusoid location, and temporal compensation of the sinusoid components before the reconstruction of the lost signal can be performed.

[0010] In such a case, two short FFTs (located on each side of the overall prototype frame) can also be used for the spectral transient detector (detecting onsets and offsets). Therefore, all replacements must be accurate enough to be used for that purpose as well.

[0011] Further information on how the phase ECU PLC operates can be found in International Patent Application No. WO2014123471 (see Appendix 1) and in 3GPP TS 26.447 V15.0.0 clause 5.4.3.5. Summary of the Invention

[0012] Various embodiments of the inventive concepts disclosed herein arise from the current realization that in some cases, a short FFT substantially increases the complexity in the first error frame, which already has a high complexity since it is used to perform the complete PLC chain of spectral analysis, sinusoid location, and temporal compensation of the sinusoid components before reconstruction of the lost signal can be performed.

[0013] According to some embodiments of the inventive concept, there is provided a method for controlling a concealment method for a lost audio frame associated with a received audio signal. In such a method, a first audio frame of the received audio signal is decoded to obtain Modified Discrete Cosine Transform (MDCT) coefficients. A first spectral shape value is determined based on MDCT coefficients decoded from the decoded first audio frame, the first spectral shape including a certain number of sub-bands. A second audio frame of the received audio signal is decoded to obtain MDCT coefficients for a second audio frame. A second spectral shape value is determined based on MDCT coefficients decoded from the decoded second audio frame, the second spectral shape including the said number of sub-bands. The first spectral shape value and the first frame energy of the first audio frame are transformed into a first representation of a first Fast Fourier Transform (FFT) based spectral analysis, and the second spectral shape value and the second frame energy of the second audio frame are transformed into a second representation of a second FFT based spectral analysis. A transient condition is detected based on the first representation of the first FFT and the second representation of the second FFT. In response to detecting the transient condition, the concealment method is modified by selectively adjusting a spectral magnitude of the substitute frame spectrum.

[0014] According to some embodiments of the inventive concept, a decoder apparatus is provided for controlling a concealment method for a lost audio frame associated with a received audio signal. The decoder apparatus decodes a first audio frame of the received audio signal to obtain Modified Discrete Cosine Transform (MDCT) coefficients. The decoder apparatus determines a first spectral shape value based on decoded MDCT coefficients from the decoded audio frame, the first spectral shape comprising a number of sub-bands. The decoder apparatus decodes a second audio frame of the received audio signal. The decoder apparatus determines a second spectral shape value based on decoded MDCT coefficients from the decoded second audio frame, the second spectral shape comprising a number of sub-bands. The decoder apparatus converts the first spectral shape value and the first frame energy of the first audio frame into a first representation of a first Fast Fourier Transform (FFT) based spectral analysis and converts the second spectral shape value and the second frame energy of the second audio frame into a second representation of a second FFT spectral analysis. The decoder apparatus detects a transient based on the first representation of the first FFT and the second representation of the second FFT, and in response to detecting the transient, the decoder apparatus modifies the concealment method by selectively adjusting a spectral magnitude of the substitute frame spectrum.

[0015] In accordance with some embodiments of the inventive concepts, there is provided a computer-processed method for controlling a concealment method for a lost audio frame associated with a received audio signal. The method includes decoding a first audio frame of the received audio signal based on a Modified Discrete Cosine Transform (MDCT). The method further includes determining a first spectral shape value based on decoded MDCT coefficients from the decoded audio frame, and calculating the calculated first spectral shape value as shape. old The method further comprises determining a first frame energy of the acoustic frame and storing the calculated first frame energy in a buffer, the first spectral shape including a number of subbands. oldThe method includes storing the calculated first spectral shape value in a buffer. The method includes decoding a second acoustic frame of the received acoustic signal. The method further includes storing the calculated first spectral shape value in a buffer. old Shape from buffer oold The method further comprises transferring the calculated first frame energy to a buffer. old E_w from the buffer oold The method further includes determining a second spectral shape value based on the decoded MDCT coefficients from the decoded second acoustic frame, and calculating the calculated second spectral shape value as shape old and storing the second frame energy in a buffer, the second spectral shape including the number of subbands. The method further comprises determining (915) a second frame energy of the second acoustic frame and describing the calculated second frame energy as E_w old and storing the first spectral shape value and the first frame energy in a buffer. The method includes converting the first spectral shape value and the first frame energy into a first representation of a spectral analysis based on a first Fast Fourier Transform (FFT), and converting the second spectral shape value and the second frame energy into a second representation of a second FFT spectral analysis. The method includes detecting a condition that may result in suboptimal reconstruction quality of a substitute frame for a lost acoustic frame when a concealment method is used to create a substitute frame for the lost acoustic frame based on the first representation of the first fast FFT and the second representation of the second FFT. The method includes modifying (921) the concealment method by selectively adjusting a spectral magnitude of the substitute frame spectrum in response to detecting the condition.

[0016] The advantage of the technique in various embodiments described below is that it can avoid using two short FFTs. This is important because it directly reduces the complexity of the first lost frame and the processing overhead of processing the lost frame. The first lost frame has high complexity because it requires both a rather long FFT of the prototype frame and an equally long inverse FFT of the reconstructed spectrum.

[0017] The accompanying drawings, which are included to provide a further understanding of this disclosure and are incorporated in and constitute a part of this application, illustrate certain non-limiting embodiments of the inventive concepts. [Brief description of the drawings]

[0018] [Figure 1] FIG. 1 is a block diagram of an audio encoder and an audio decoder using a network. [Diagram 2] FIG. 2 is a block diagram of a decoder including packet loss concealment. [Diagram 3] 1 is a flowchart of operations and methods that may be for updating spectral parameters during good frames in accordance with some embodiments of the inventive concepts. [Figure 4] 1 is a flowchart of operations and methods that may be implemented to update spectral parameters during good frames in accordance with some embodiments of the inventive concepts. [Diagram 5] 1 is a flow chart for updating spectral parameters during a bad frame in accordance with some embodiments of the inventive concepts. [Figure 6] FIG. 13 is a signal diagram of the PLC prototype buffer and the location of a short FFT for transient detection when the next frame is lost. [Figure 7] 13A-13C are signal diagrams of a PLC prototype buffer for transient detection when the next frame is lost, and the spectral shape and location of frame energy, according to some embodiments. [Figure 8] FIG. 1 is a diagram illustrating the difference in frequency resolution and bin center points for different transforms related to PLC. [Figure 9] 1 is a flow chart illustrating a modification of a concealment method according to some embodiments of the inventive concepts. [Figure 10] 1 is a flow chart illustrating a modification of a concealment method according to some embodiments of the inventive concepts. [Figure 11] 1 is a flow chart illustrating a modification of a concealment method according to some embodiments of the inventive concepts. [Figure 12] FIG. 2 illustrates an example of a decoder device according to some embodiments of the inventive concepts. [Figure 13] FIG. 2 illustrates another example of a decoder device according to some embodiments of the inventive concepts. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0019] The inventive concept will now be described more fully with reference to the accompanying drawings, in which examples of embodiments of the inventive concept are shown. However, the inventive concept may be embodied in many different forms and should not be construed as being limited to the embodiments set forth herein. Instead, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the inventive concept to those skilled in the art. It should also be noted that these embodiments are not mutually exclusive. It may be implicitly assumed that elements from one embodiment are also present / used in another embodiment.

[0020] The following description provides various embodiments of the disclosed subject matter, which are provided as instructional examples and should not be construed as limiting the scope of the disclosed subject matter, for example, certain details of the described embodiments can be modified, omitted, or expanded without departing from the scope of the described subject matter.

[0021] The inventive concepts described herein reduce the complexity of PLC. These embodiments relate to reducing the complexity of embodiments where the approach used for packet concealment is sinusoidal modeling in the frequency domain by using an additional low-resolution background noise model to better handle burst errors. In this case, the approach progresses from a sinusoidal model to a low-resolution noise model over longer error bursts. The low-resolution noise model can be updated based on a stored prototype frame during the first error frame. The described techniques can also be used to determine high-quality (and low-complexity) frequency band predictions of the energy decay over time for signals in various frequency bands, which can be used to model the band energy of the concealed frames.

[0022] If the speech / audio compression is performed in the frequency domain, there may already be a spectral representation available in the frequency domain, most often the Modified Discrete Cosine Transform (MDCT) domain. The coefficients of the spectral representation available in many situations can be used to create an alternative spectral shape to replace the complexity of a short FFT. For example, the spectral shape of the first frame error can be used to create a spectral prediction that corresponds to the spectral prediction that would have been generated by a short FFT.

[0023] In the embodiments described herein, the available MDCT coefficients can be used to provide the spectral shape, while the energy (i.e., level) for the spectral prediction is based on the energy of the windowed prototype frame. However, the inventors have come to realise that using only the MDCT coefficients for both shape and level has been found to provide a poor quality prediction for the two short FFTs that are to be replaced.

[0024] The advantage of the technique described below is that it is possible to avoid the use of two short FFTs, which is important because it directly reduces the complexity of the first lost frame, which has a high complexity because it requires both a rather long FFT of the prototype frame and an equally long inverse FFT of the reconstructed spectrum.

[0025] Although the MDCT coefficients available to the decoder do not provide a stable energy prediction, the coefficients can be used for spectral shape prediction. The energy of a windowed prototype frame can be used to obtain the levels for the spectral prediction, since this can provide a better prediction of the actual FFT spectrum.

[0026] By avoiding the complexity of using two shorter FFTs, slight differences in both temporal and spectral characteristics may occur that are not significant for use in the form of long-term prediction of the background signal, and these slight differences are not of major concern for transient detector energy decay prediction.

[0027] The inventive concept of reusing the MDCT coefficients (or any other spectral domain information available in the normal coded domain) and a transform into a spectral shape that can be used in place of two short FFT transforms reduces the complexity and processing overhead of handling lost frames, which also involves how the MDCT coefficients are sorted into a format that approximates the FFT bins as closely as possible.

[0028] The decoder device may consist of two units, or may be part of the PLC illustrated in FIG. 2 or the decoder device illustrated in FIG. 12 and FIG. 13. The decoder (1201, 1301) can update the spectral shape and frame energy during error-free operation. The decoder (1201, 1301) can use the saved spectral shape and frame energy during the first frame error to generate a long-term spectral prediction to be used during error concealment. The third component of the decoder (1201, 1301) can also be used to determine the frequency band collapse to be applied in the PLC reconstruction, such as when there is a significant drop in energy.

[0029] Reusing MDCT coefficients typically only produces one spectral shape per frame. Having two spectral shapes during the first error frame can be achieved by generating one spectral shape prediction per good frame and also storing the spectral shape prediction from the preceding good frame. To obtain the correct level of spectral prediction, at the end of a good frame processing in the MDCT-based decoder, the windowed energy of the corresponding PLC-prototype frame can be stored. A good frame means a correctly received, error-free frame, while a bad frame means an erased, i.e., lost or corrupted frame.

[0030] During a lost frame, the second unit uses the two stored spectral shapes and frame energies to generate two spectral predictions corresponding to the two that would have been generated by two short FFTs. This reduces complexity and processor overhead. The third unit can establish the decay factors to be used per frequency band in the PLC reconstruction of the lost frame based on the stored shapes and energies. After this, normal processing of the phase ECU continues as described above, see International Patent Application No. WO2014123471 (Appendix 1) or 3GPP TS 26.447 V15.0.0 clause 5.4.3.5.

[0031] The techniques described herein are not limited to the use of spectral prediction from the MDCT as described above, but can be adapted to work with any other spectral prediction technique used in the codec.

[0032] The following describes in more detail the functions that use the MDCT.

[0033] To obtain the MDCT coefficients, the MDCT is acquired over a 20 ms window with a 10 ms advance. When using one transform, e.g., the MDCT, to perform subband prediction of another transform, e.g., the FFT, it is important to perform the classification into subbands over the correct coefficients. The PLC prototype frame saved after the good frame is 16 ms long, and the transient detector subband analysis module uses two short FFTs of length 4 ms, which is a quarter of the PLC prototype frame. The actual length of these items depends on the sampling frequency used and can be from 8 kHz to 48 kHz. These lengths affect the number of spectral bins in the individual transforms. Using the results of the two short FFT analyses, the transformation factor μ is determined as explained below.

[0034] Spectral shape history update in good frames For transient analysis, the Phase ECU can use the MDCT-based spectral shape and the history of the MDCT-synthesis windowed energy to build a picture of how the input signal has evolved over time. The spectral shape is calculated based on the decoded MDCT coefficients, which hold the spectral representation of the decoded signal. The spectral shape consists of subbands, where the number of subbands N grp depends on the sampling frequency as shown in Table 1. JPEG0007660628000001.jpg64170

[0035] In case of a good frame, i.e. if the bad frame indicator indicates that the frame is not a bad frame (e.g. BFI=0), the values ​​of the spectral shape and the frame energy can be updated. These steps are illustrated in the flow chart of FIG. 3. Referring to FIG. 3, in operation 301, a determination is made whether BIF=0. Note that the parameters can only be calculated for the current frame. If the previous frame was a good frame, the values ​​stored during the last frame are stored in a designated buffer (i.e. shape ) for the second and last frame. oold In a subsequent operation 303, the spectral shape from the last frame is old (k) is the second buffer shape oold It is transferred to and stored in (k). shape oold (k)=shape old (k), 0≦k <N grp (1)

[0036] Similarly, in operation 305, the last frame energy is stored in a second buffer E_w oold will be transferred to. E_w oold =E_w old (2)

[0037] These updates are followed by operations 307 and 309 to calculate the spectral shape for the final frame buffer. old (k) and frame energy E_w old A new value of can be calculated. Table 2 illustrates by way of example how the bins of the current MDCT coefficients can be divided among the sub-bands. The table entries in Table 2 show the starting coefficients of the individual sub-bands for one embodiment that can be used in the method described in International Application WO 2014 / 123471. Other sub-bands can be used for other embodiments. JPEG0007660628000002.jpg38170

[0038] It is sometimes desirable to have a spectral shape in the range [0,...1], based on subbands. This means that This can be achieved by first calculating the overall magnitude (q_d(n)) of the MDCT coefficients as MDCT is the number of MDCT coefficients and depends on the sampling frequency, such as the example sampling frequencies shown in Table 3. JPEG0007660628000004.jpg62170

[0039] Then, using the value calculated for shape_tot, The spectral shapes of the individual subbands can be normalized, which can be determined as TIFF0007660628000005.tif20170, and the above formula forms the spectral shape prediction for the new values ​​of the last frame. Note that there may be some MDCT coefficients that are not assigned to a spectral shape. This is due to not using the DC bin for the corresponding short FFT.

[0040] In order to be able to use the spectral shape during reconstruction, the frame energy can be calculated based on the windowed prototype frames. This is TIFF0007660628000006.tif20170, and the above formula can be used to determine w whr may be a long FFT spectrum analysis window (especially for long-term background approximation prediction), and x prev is the phase ECU time domain prototype signal used to create a replacement for a potentially lost frame, and L prot is the time window w whr The length of x corresponds to the same prev The number of samples in the signal.

[0041] In an alternative embodiment, the overall spectral approximation performance can be balanced between providing a good background prediction and a good prediction for transient offset detection. whr This can be implemented in one embodiment by changing the window to an optional long / different window than the 16ms FFT spectrum analysis window. whr One possible approach to modifying the window is to shorten the window and shift the energy prediction window into the future so that the energy prediction is more time-aligned with the energy content of the short (4 ms) FFT window. This approach also reduces the complexity of the energy alignment calculation. For example, old to the windowed energy of the most recent synthesized samples, 3*L_prot / 4 (12 ms), and even more to the most recent samples, L_prot / 2 (8 ms). This allows balancing the spectral approximation between the background prediction (targeting a total spectral period of 16 ms) and the transient offset prediction (targeting the last 4 ms).

[0042] Referring to FIG. 4, in order to avoid using old values ​​in the secondary buffer after a bad frame or a burst of bad frames, the shape of the spectrum and the frame energy are oold (k) and E_w ooldThe state can be reinitialized so that a good frame BFI=0 (illustrated by operation 401) is replaced by a bad frame BFI prev If followed by =1 (illustrated by operation 403), the calculated value is copied to the secondary buffer as described in the first equations (1) and (2) in operations 405 and 407, respectively.

[0043] Transformation of Spectral Shape into Short FFT Subband Energy For transient analysis, the stored spectral shape and frame energy can be used to analyze how the subband energy evolves over time. These values ​​are used for two things: firstly for the subband transient detector, and secondly for the long term average can be used to form TIFF0007660628000007.tif7170, TIFF0007660628000008.tif7170 can be used to adjust the subband energies during an error burst. These values ​​form the basis for calculating signal correction values ​​that are used during error bursts. JPEG0007660628000009.jpg48170

[0044] Using the spectral shape and frame energy, an approximation of the subband energy for the last two error-free frames is generated. This is illustrated in the flow chart of Figure 5 where the bad frame indicator indicates a bad frame (i.e., BFI=1) in operation 501. Referring to Figure 5, the first frame represents the subband frame energy before the last frame, and in operation 503, E oold (k)=μ·shape oold (k)·E_w oold , 0≦k <N grp (6) It can be generated by:

[0045] The second subframe frame energy is for the last frame, TIFF0007660628000010.tif10170, where μ is a scalar constant that depends on the sampling frequency, and the conversion of the MDCT-based spectral shape to an approximation of the FFT-based spectral analysis. We are dealing with TIFF0007660628000011.tif7170. An example of μ for various fs frequencies is shown in Table 5. JPEG0007660628000012.jpg62170

[0046] The transformation factor μ can be calculated offline and depends on the MDCT window and the window used for the FFT, which serves as an approximation during lost frame reconstruction. To find these coefficients, one must run the PLC using both methods available for calculating the transformation factors: the original FFT analysis and the reduced complexity approximation of the FFT using the MDCT. A convenient way to calculate the transformation factors is to use a sine wave. One can use a single wave in the center of each group interval and start the calculation with the coefficient set to 1. The correct value can be calculated by comparing the two methods. Note that the bins in Table 4 show the binning for an FFT with an analysis length that is one-quarter of the analysis length used for the spectral analysis used by the PLC on the prototype frame, i.e., if the spectral analysis is done using a 16 ms FFT, the binning is for a 4 ms spectral analysis.

[0047] Figure 6 illustrates an overview of how the framing and related frame structure of the MDCT coder is applied for an asymmetrically positioned MDCT window with look-ahead zeros - LA_ZEROS segments. Note that the signal diagram shows that due to the use of look-ahead zeros (LA_ZEROS - 3 / 8 of the frame length) in the MDCT window, the frame is only decoded up to a point 3 / 4 of the way through the current frame. The framing affects what part of the current frame is decodable and therefore the position of the PLC prototype frame that is saved and used if the next frame is lost.

[0048] 6 also illustrates the difference in the length of the involved transforms used in this embodiment: in the MDCT, which has a length twice that of the encoded frame, each spectral point is still represented using two coefficients (compared to the FFT, where N samples yield N complex numbers, i.e. 2N scalar values), where one may be the time-reversal of the other.

[0049] Figure 7 illustrates an overview of how the framing and related frame structure of the MDCT coder is applied to determine the subband energy and spectral shape as described above. Figure 7 illustrates that the current frame and the previous frame are good frames, and shows that the methods of Figures 3 and 9-11 can be implemented in relation to the encoding process.

[0050] Figure 8 illustrates a graphical representation of the different spectral representations. The PLC spectral analysis is done on a 16 ms time segment, which results in a bin spacing of 62.5 Hz. From an N-point FFT, N / 2+1 bins are obtained, where the start is 0 Hz and the end is fs / 2 (1 / 2 the sampling frequency). It is similar for the transient analysis, where a shorter FFT is substituted, the difference is that the time window is 4 ms, which results in a bin spacing of 250 Hz. For the MDCT done over a 20 ms time segment, the bin spacing is 100 Hz after sorting the time coefficients and time-reversed coefficients for the length of M MDCT and M MDCT, and after sorting there are M / 4 coefficients. Since the MDCT does not have a DC or fs / 2 coefficient, the simplest representation is to have a 1 / 2 bin offset as shown in Figure 8.

[0051] In one embodiment, as described in International Patent Application WO2014123471 (see Appendix 1), these predictions of the spectral predictions for transient analysis as described above can be used to replace the spectral predictions used for transient calculations and concealment adaptation. These predictions can also be used in other situations where spectral predictions are used such as in 3GPP TS 25.447 V. 15.0.0.

[0052] For example, referring to FIG. 9, the decoder (1201, 1301) may decode a first acoustic frame of a received acoustic signal based on the MDCT in operation 901. In operation 903, the decoder (1201, 1301) determines a value of a first spectral shape based on MDCT coefficients from the decoded first acoustic frame, and refers to the determined value of the first spectral shape as shape old The first spectral shape may be stored in a buffer, and the first spectral shape may include a number of subbands. In operation 905, the decoder (1201, 1301) determines a first frame energy of the first acoustic frame and represents the determined first frame energy as E_w oldIt can be stored in a buffer.

[0053] In operation 907, the decoder (1201, 1301) may decode a second acoustic frame of the received acoustic signal based on the MDCT. In operation 909, the decoder (1201, 1301) may convert the determined value of the first spectral shape into a shape old Shape from buffer oold The operation 909 may correspond to the operation 303 in FIG. 3. In operation 911, the decoder (1201, 1301) may transfer the determined first frame energy to the buffer E_w old E_w from the buffer oold Operation 911 may correspond to operation 305 in FIG.

[0054] In operation 913, the decoder (1201, 1301) determines a value of a second spectral shape based on the decoded MDCT coefficients from the decoded second acoustic frame, and defines the determined value of the second spectral shape as shape old The second spectral shape may be stored in a buffer, the second spectral shape including the number of subbands. In operation 915, the decoder (1201, 1301) determines a second frame energy of the second acoustic frame and refers to the calculated second frame energy as E_w old It can be stored in a buffer.

[0055] In operation 917, the decoder (1201, 1301) may convert the first spectral shape values ​​and the first frame energies into a first representation of a first Fast Fourier Transform (FFT) based spectral analysis, and may convert the second spectral shape values ​​and the second frame energies into a second representation of a second FFT spectral analysis.

[0056] In operation 919, the decoder (1201, 1301) can detect, based on the transformed value of the first spectral shape and the value of the second spectral shape, a condition that may result in suboptimal reconstruction quality of a substitute frame for a lost acoustic frame if a concealment method is used to create a substitute frame.

[0057] In operation 921, the decoder (1201, 1301) may, in response to detecting the above condition, modify the concealment method by selectively adjusting the spectral magnitude of the substitute frame spectrum.

[0058] In one embodiment, the spectral prediction described above can be used to reduce the complexity and processing overhead in transient calculations and concealment adaptation as described in International Patent Application No. WO2014123471 and 3GPP TS 25.447 V. 15.0.0 clause 5.4.3.5. oold (k) and E old (k) is used to calculate the energy ratio prediction, and E oold (k) and E old Transient detection can be performed using the (k) bins. For example, referring to FIG. 10, in operation 1001, E oold (k) and E old The frequency group selective transient detection can now be used to determine the subband energy of E oold (k) and E old (k) may be based on the band-wise ratio between the respective band energies of the frames associated with it. TIFF0007660628000013.tif13170 Other ratios are also possible.

[0059] Interval Ik=[m k-1 +1,...,m k ] is the frequency band TIFF0007660628000014.tif10170, where fs is the audio sampling frequency and N partNote that m corresponds to the frame size. The lowest lower frequency band limit m0 can be set to 0, but it can also be set to a DFT index corresponding to a higher frequency to mitigate prediction errors that are large at lower frequencies. The highest upper frequency band limit m K teeth TIFF0007660628000015.tif12170, but is preferably still selected to correspond to somewhat lower frequencies where transients have a significant audible effect.

[0060] The ratios can be compared against certain thresholds, for example a respective upper threshold for (frequency selective) onset detection 1003 and a respective lower threshold for (frequency selective) offset detection 1005. If the energy ratio exceeds the upper threshold or is below the lower threshold, the concealment method can be modified in operation 1007. These operations correspond to operation 919 in FIG. 9.

[0061] An example of modifying the concealment method of operation 921 of Figure 9 is illustrated in Figure 11. In this embodiment of the modified concealment method, the magnitude and phase of the substitute frame spectrum are determined. The magnitude is modified by scaling using two factors α(m) and β(m), and the phase is modified using an additional phase component θ(m). This leads to the calculation of the substitute frame. TIFF0007660628000016.tif8170In the above formula, Z(m) is the replaced frame spectrum, α(m) is the first magnitude attenuation factor, β(m) is the second magnitude attenuation factor, Y(m) is the prototype frame, and θ k is the phase shift and θ(m) is the additive phase component.

[0062] In this embodiment, the number of consecutively observed frame losses n burstis determined, where a burst loss counter is incremented by one for every frame loss and reset to zero upon receipt of a valid frame. The magnitude adaptation in operation 1101 is performed by incrementing the burst loss counter n burst is some threshold thr burst , e.g. thr burst = 3, in which case a value smaller than 1 is used for the damping factor, for example α(m) = 0.1. Further adaptation of the magnitude damping factor is performed using the indicator R old\oold,band (k) or alternatively R old\oold (m) or R old\oold A suitable adaptive action in operation 1107 may be to modify the second magnitude damping factor β(m) such that the total damping is controlled by the product of the two factors α(m)·β(m).

[0063] β(m) can be set in response to an indicated transient. If an offset is detected, the factor β(m) can be selected to reflect the energy reduction of the offset. A suitable choice is to set β(m) to the detected gain change. TIFF0007660628000017.tif10170

[0064] It has been found to be advantageous to limit, if anything, the energy increase in the substitute frame when an onset is detected: in that case the factor can be set to some fixed value, for example 1, which means that there is no attenuation, but also no amplification at all.

[0065] Examples of phase dithering in operation 1109 are described in International Patent Application No. WO2014123471 (see Appendix 1) and in 3GPP_TS_26.447_v.15.0.0_2018_06, clause 5.4.3.5.3 and need not be described in detail here.

[0066] Fig. 12 is a schematic block diagram of a decoder that can be used according to the embodiments. The decoder 1201 comprises an input unit 1203 configured to receive an encoded audio signal. Fig. 11 illustrates frame loss concealment by a logical frame loss concealment unit 1205, showing that the decoder is configured to realize concealment of lost audio frames according to the embodiments described above. Furthermore, the decoder comprises a controller 1207 for realizing the embodiments described above, including the operations illustrated in Figs. 3-5 and 9-11 and / or discussed below in relation to the respective exemplary embodiments.

[0067] For example, the controller 1207 can be configured to determine characteristics of an already received and reconstructed audio signal, or statistical characteristics of an observed frame loss, where replacement of the lost frame by the original non-adaptive phase ECU method provides a relatively reduced quality. If such a condition is detected, the controller 1207 can be configured to modify elements of the concealment method according to the replacement frame spectrum calculated by selectively adjusting the phase or spectral magnitude as described above, and output the audio frame to a receiver for playback. The receiver can be a device having a loudspeaker, a loudspeaker device, a telephone, and so on.

[0068] The decoder can be realized in hardware. There are many different circuitry elements that can be used and combined to achieve the functions of the units of the decoder. Such variations are encompassed by the embodiments. Particular examples of hardware implementations of the decoder are digital signal processor (DSP) hardware, and implementations in integrated circuit technologies, including both general purpose electronic circuitry and application specific circuitry.

[0069] The decoder described herein may alternatively be implemented as illustrated in FIG. 13 for reconstruction of an audio signal including performing audio frame loss concealment according to the embodiments described herein as shown in FIGS. 3-5 and 9-11, for example by one or more of a processor 1305 and suitable software 1309 with suitable storage or memory 1311 therefor. An incoming encoded audio signal is received by an input (IN) 1303 to which the processor 1305 and the memory 1311 are connected. The decoded and reconstructed audio signal obtained from the software is output from an output (OUT) 1307 towards a receiver for playback. As discussed herein, the operations of the decoder 1301 may be implemented by the processor 1305. Furthermore, modules may be stored in the memory 1311 and these modules may provide instructions such that when the instructions of the modules are executed by the processor 1305, the processor 1305 performs the respective operations.

[0070] The techniques described above may be used, for example, in receivers that may be used in mobile devices (eg, mobile phones, laptop computers), or in fixed devices such as personal computers.

[0071] It should be understood that the selection of interaction units or modules, as well as the naming of the units, is for illustrative purposes only and can be configured in a number of alternative ways to enable the disclosed process actions to be performed.

[0072] Abbreviation At least some of the following abbreviations may be used in this disclosure. In the event of a conflict between the abbreviations, the manner in which the abbreviation is used above shall prevail. If listed multiple times below, the first listing shall prevail over all subsequent listings. Abbreviation Description ADC Analog-to-Digital Converter BFI Bad Frame Indicator BFI_prev Bad frame indicator for the previous frame DAC Digital to Analog Converter FFT Fast Fourier Transform MDCT Modified Discrete Cosine Transform References [1] International Patent Application No. WO2014123470 [2] International Patent Application No. WO2014123471 [3] 3GPP TS 26.445 V15.1.0 (clauses 5.3.2.2 and 6.2.4.1), which is incorporated herein by reference in its entirety. [4] 3GPP TS 26.447 V15.0.0 (clause 5.4.3.5), which is incorporated herein by reference in its entirety.

[0073] Listing of Exemplary Embodiments In the following, exemplary embodiments are discussed, in which reference numbers / letters are provided in parentheses as an example / illustration without limiting the exemplary embodiments to the particular elements designated by the reference numbers / letters. 1. A method by a computer processor for controlling a concealment method for a lost audio frame of a received audio signal, the method comprising: Decoding (901) a first audio frame of a received audio signal based on a modified discrete cosine transform (MDCT); determining (307-309, 903) a value of a first spectral shape based on the decoded MDCT coefficients from the decoded acoustic frame, and calculating the calculated first spectral shape value as shape old storing in a buffer a value of a first spectral shape, the first spectral shape including a number of sub-bands; Determining a first frame energy of the acoustic frame (905) and denoting the calculated first frame energy as E_w old storing in a buffer; Decoding (907) a second audio frame of the received audio signal; The calculated first spectral shape value is called shape. old Shape from buffer oold Moving it to the buffer (303, 909) The calculated first frame energy is E_w old E_w from the buffer oold Moving to a buffer (305, 911) determining (307-309, 913) a second spectral shape value based on the decoded MDCT coefficients from the decoded second acoustic frame, and calling the calculated second spectral shape value shape old storing in a buffer a value of a second spectral shape, the second spectral shape including the number of sub-bands; Determining a second frame energy of the second acoustic frame (915) and denoting the calculated second frame energy as E_w old storing in a buffer; Transforming the first spectral shape values ​​and the first frame energies into a first representation of a first Fast Fourier Transform (FFT) based spectral analysis (917), and transforming the second spectral shape values ​​and the second frame energies into a second representation of a second FFT based spectral analysis (917); Detecting (919) a condition that may result in a suboptimal reconstruction quality of a substitute frame for a lost acoustic frame when a concealment method is used to create a substitute frame for the lost acoustic frame based on the first representation of the first fast FFT and the second representation of the second FFT; modifying (921) the concealment method by selectively adjusting the spectral magnitude of the substitute frame spectrum in response to detecting the condition; Includes. 2. In the method of embodiment 1, determining a first spectral shape value based on the decoded MDCT coefficients includes: determining (307) the overall magnitude of the MDCT coefficients; normalizing individual subband values ​​of the first spectral shape; storing each normalized subband value as one of the first spectral shape values; Includes. 3. The method of embodiment 2, wherein the overall magnitude of the MDCT coefficients is: TIFF0007660628000018.tif20170, where shape_tot is the total size of the MDCT coefficients and N MDCT is the number of MDCT coefficients and depends on the sampling frequency, and q_d(n) is the MDCT coefficient. 4. In any of the methods of embodiments 2-3, the normalization of each subband comprises: It is normalized according to TIFF0007660628000019.tif21170 and the shape is calculated by the formula above. old where (k) is the spectral shape of subband (k), shape_tot is the total magnitude of the MDCT coefficients, q_d(n) is the MDCT coefficient, N grp is the number of MDCT coefficients, grp_bin(k) is the starting index for the MDCT coefficients in subband(k), and N grp is the number of subbands. 5. The method of any one of the first to fourth embodiments, wherein the frame energies of the first frame energy and the second frame energy are: It is determined according to TIFF0007660628000020.tif18170, and E_w old is the frame energy, and w whr is the long FFT spectrum analysis window, and x prev is the time-domain prototype signal used to generate replacements for potentially lost frames, and L prot x prev The number of samples in the signal. 6. In any of the methods of embodiments 1 to 5, converting the first spectral shape values ​​and the first frame energies to a first representation of a first fast FFT based spectral analysis, and converting the second spectral shape values ​​and the second frame energies to a second representation of a second FFT spectral analysis includes applying a conversion factor to the first spectral shape values ​​and the first frame energies, and the second spectral shape values ​​and the second frame energies. 7. The method of embodiment 6, wherein the conversion factor depends on the sampling frequency of the decoding. 8. The method of any one of embodiments 4 to 7, comprising: E oold (k) is the first representation, μ is the transformation factor, and shape oold (k) is the spectral shape of subband (k) of the first spectral shape, and E_w oold is the first frame energy, and E old (k) is the second representation, and shape old (k) is the spectral shape of subband (k) of the second spectral shape, and E_w old is the second frame energy, and N grp is the number of subbands, E oold (k)=μ·shape oold (k)·E_w oold , 0≦k <N grp and Converting the first spectral shape values ​​and the first frame energies into a first representation of a first fast FFT based spectral analysis according to TIFF0007660628000021.tif8170, and converting the second spectral shape values ​​and the second frame energies into a second representation of a second FFT based spectral analysis according to TIFF0007660628000021.tif8170. Further includes: 9. The method of embodiment 8, comprising: E oold (k) and E old determining (1105) whether the subband transient exceeds a threshold based on (k); modifying the concealment method by selectively adjusting (1107) the spectral magnitude of the substitute frame spectrum in response to the subband transient exceeding the threshold; Further includes: 10. The method of embodiment 9, wherein the permuted frame spectrum is: Calculated according to the formula in TIFF0007660628000022.tif8170, Adjusting the magnitude of the spectrum also includes adjusting β(m) (1107), where Z(m) is the permuted frame spectrum, α(m) is a first magnitude attenuation factor, β(m) is a second magnitude attenuation factor, Y(m) is the prototype frame, and θ k is the phase shift and θ(m) is the additive phase component. 11. The method of any one of embodiments 1 to 10, comprising: Receiving a bad frame indicator (403, 501); In response to receiving a bad frame indicator, oold Buffer and E_w oold Flushing the energy buffer; and receiving a new audio frame of the received audio signal; determining (503) a new spectral shape value based on the decoded MDCT coefficients from the decoded new acoustic frame; and calculating the calculated new spectral shape value as shapeold Buffers and shapes oold storing (405) in a buffer, the new spectral shape including a number of subbands; determining (505) a new frame energy of the acoustic frame, and expressing the calculated new frame energy as E_w old Buffer and E_w oold Storing in a buffer (407) Further includes: 12. A decoder device (1201, 1301) adapted to perform operations according to any of the embodiments 1 to 11. 13. A decoder device (1201, 1301) configured to control a concealment method for a lost audio frame of a received audio signal, the decoder device comprising: Decoding a first audio frame of the received audio signal based on a modified discrete cosine transform (MDCT); determining a first spectral shape value based on the decoded MDCT coefficients from the decoded acoustic frame; and calculating the calculated first spectral shape value as shape old storing the first spectral shape in a buffer, the first spectral shape including a number of subbands; A first frame energy of the acoustic frame is determined, and the calculated first frame energy is denoted as E_w old Store it in a buffer, Decoding a second audio frame of the received audio signal; The calculated first spectral shape value is called shape. old Shape from buffer oold Move it to the buffer, The calculated first frame energy is E_w old E_w from the buffer oold Move it to the buffer, determining a second spectral shape value based on the decoded MDCT coefficients from the decoded second acoustic frame, and calculating the calculated second spectral shape value as shape oldstoring the second spectral shape in a buffer, the second spectral shape including the number of sub-bands; A second frame energy of the second acoustic frame is determined, and the calculated second frame energy is designated as E_w old Store it in a buffer, Transforming the first spectral shape values ​​and the first frame energies into a first representation of a first Fast Fourier Transform (FFT) based spectral analysis and transforming the second spectral shape values ​​and the second frame energies into a second representation of a second FFT based spectral analysis; detect a condition that may result in suboptimal reconstruction quality of a substitute frame for a lost acoustic frame when a concealment method is used to create a substitute frame for the lost acoustic frame based on a first representation of the first fast FFT and a second representation of the second FFT; and In response to detecting the condition, modifying the concealment method by selectively adjusting the spectral magnitude of the substitute frame spectrum. It is set as follows. 14. A decoder device according to embodiment 13, the decoder device being configured to perform the operations of embodiments 2-11. 15. A decoder device (1201, 1301) configured to control a concealment method for a lost audio frame of a received audio signal, the decoder device comprising: A processor (1305); A memory (1311), which, when executed by a processor, causes a decoder device (1201, 1301) to: Decoding (901) a first audio frame of a received audio signal based on a modified discrete cosine transform (MDCT); Determining (903) a value of a first spectral shape based on the decoded MDCT coefficients from the decoded acoustic frame, and calculating the calculated first spectral shape value as shape old storing in a buffer a value of a first spectral shape, the first spectral shape including a number of sub-bands; Determining a first frame energy of the acoustic frame (905) and denoting the calculated first frame energy as E_w old storing in a buffer; Decoding (907) a second audio frame of the received audio signal; The calculated first spectral shape value is called shape. old Shape from buffer oold Moving it to the buffer (303, 909) The calculated first frame energy is E_w old E_w from the buffer oold Moving to a buffer (305, 911) determining (307-309, 913) a second spectral shape value based on the decoded MDCT coefficients from the decoded second acoustic frame, and calling the calculated second spectral shape value shape old storing in a buffer a value of a second spectral shape, the second spectral shape including the number of sub-bands; Determining a second frame energy of the second acoustic frame (915) and denoting the calculated second frame energy as E_w old storing in a buffer; Transforming (917) the first spectral shape values ​​and the first frame energies into a first representation of a first Fast Fourier Transform (FFT) based spectral analysis, and transforming (917) the second spectral shape values ​​and the second frame energies into a second representation of a second FFT based spectral analysis; Detecting (919) a condition that may result in a suboptimal reconstruction quality of a substitute frame for a lost acoustic frame when a concealment method is used to create a substitute frame for the lost acoustic frame based on the first representation of the first fast FFT and the second representation of the second FFT; modifying (921) the concealment method by selectively adjusting the spectral magnitude of the substitute frame spectrum in response to detecting the condition; A memory (1311) for storing instructions for performing operations including Equipped with. 16. In the decoder device of embodiment 1, to determine a first spectral shape value based on the decoded MDCT coefficients, the instructions, when executed by the processor, cause the device to: determining (307) the overall magnitude of the MDCT coefficients; normalizing individual subband values ​​of the first spectral shape; storing each normalized subband value as one of the first spectral shape values; The method further includes instructions for performing operations including: 17. In the decoder device of embodiment 16, the total magnitude of the MDCT coefficients is: TIFF0007660628000023.tif20170, where shape_tot is the total size of the MDCT coefficients and N MDCT is the number of MDCT coefficients and depends on the sampling frequency, and q_d(n) is the MDCT coefficient. 18. In any one of the decoder devices of embodiments 16 to 17, the normalization of each subband is performed by: It is normalized according to TIFF0007660628000024.tif20170 and the shape is calculated by the above formula. old where (k) is the spectral shape of subband(k), shape_tot is the total magnitude of the MDCT coefficients, q_d(n) is the MDCT coefficients, grp_bin(k) is the starting index for the MDCT coefficients in subband(k), and N grp is the number of subbands. 19. A decoder device according to any one of embodiments 15 to 18, wherein the frame energies of the first frame energy and the second frame energy are: Determined according to TIFF0007660628000025.tif20170, and E_w old is the frame energy, and w whr is the long FFT spectrum analysis window, and x previs the time-domain prototype signal used to generate replacements for potentially lost frames, and L prot x prev The number of samples in the signal. 20. In the decoder device of any of embodiments 15-19, the instructions, when executed by a processor, to convert the first spectral shape values ​​and the first frame energies to a first representation of a first fast FFT-based spectral analysis and convert the second spectral shape values ​​and the second frame energies to a second representation of a second FFT spectral analysis, cause the device to: applying a transformation factor to the first spectral shape values ​​and the first frame energies and to the second spectral shape values ​​and the second frame energies; The method further includes instructions for performing operations including: 21. The decoder device of embodiment 20, wherein the conversion factor depends on the sampling frequency of the decoding. 22. A decoder device according to any one of embodiments 20 to 21, E oold (k) is the first representation, μ is the transformation factor, and shape oold (k) is the spectral shape of subband (k) of the first spectral shape, and E_w oold is the first frame energy, and E old (k) is the second representation, and shape old (k) is the spectral shape of subband (k) of the second spectral shape, and E_w old is the second frame energy, and N grp is the number of subbands, E oold (k)=μ·shape oold (k)·E_w oold , 0≦k <N grp and Converting the first spectral shape values ​​and the first frame energies into a first representation of a first fast FFT based spectral analysis according to TIFF0007660628000026.tif9170, and converting the second spectral shape values ​​and the second frame energies into a second representation of a second FFT based spectral analysis according to TIFF0007660628000026.tif9170. Further includes: 23. The decoder device of embodiment 22, wherein the instructions, when executed by the processor, cause the device to: E oold (k) and E old determining (1105) whether the subband transient exceeds a threshold based on (k); modifying the concealment method by selectively adjusting (1107) the spectral magnitude of the substitute frame spectrum in response to the subband transient exceeding the threshold; The method further includes instructions for performing operations including: 24. The decoder device of embodiment 22, wherein the substitute frame spectrum is: Calculated according to the formula in TIFF0007660628000027.tif8170, Adjusting the magnitude of the spectrum also includes adjusting β(m) (1107), where Z(m) is the permuted frame spectrum, α(m) is a first magnitude attenuation factor, β(m) is a second magnitude attenuation factor, Y(m) is the prototype frame, and θ k is the phase shift and θ(m) is the additive phase component. 25. A decoder device according to any one of embodiments 1 to 10, wherein the instructions, when executed by the processor, cause the device to: Receiving a bad frame indicator (403, 501); In response to receiving a bad frame indicator, oold Buffer and E_w oold Flushing the energy buffer; and receiving a new audio frame of the received audio signal; determining (503) a new spectral shape value based on the decoded MDCT coefficients from the decoded new acoustic frame; and calculating the calculated new spectral shape value as shape old Buffers and shapes oold storing (405) in a buffer, the new spectral shape including a number of subbands; determining (505) a new frame energy of the acoustic frame, and expressing the calculated new frame energy as E_w old Buffer and E_w oold Storing in a buffer (407) The method further includes instructions for performing operations including:

[0074] Additional explanation Generally, all terms used herein should be interpreted according to the original meaning of those terms in the relevant technical field, unless a different meaning is clearly given and / or implied from the context in which the term is used. All references to elements, devices, components, means, steps, etc. in the singular should be interpreted broadly as meaning at least one instance of the element, device, component, means, step, etc., unless clearly stated otherwise. The steps of any method disclosed herein need not be performed in the exact order disclosed, unless a step is clearly described as following or preceding another step, and / or unless it is implied that a step must follow or precede another step. Any feature of any embodiment disclosed herein can be applied to any other embodiment, whenever appropriate. Similarly, any advantage of any embodiment can be applied to any other embodiment, and vice versa. Other objects, features, and advantages of the included embodiments will become apparent from the following description.

[0075] Some of the embodiments contemplated herein are described more fully below with reference to the accompanying drawings, however, other embodiments are encompassed within the scope of the subject matter disclosed herein and the subject matter disclosed should not be construed as being limited to only the embodiments shown herein, but rather these embodiments are provided as examples to convey the scope of the subject matter to those skilled in the art.

[0076] Any pertinent steps, methods, features, functions or advantages disclosed herein may be implemented via one or more functional units or modules of one or more virtual devices. An individual virtual device may comprise many of these functional units. These functional units may be realized via processing circuitry, which may include one or more microprocessors or microcontrollers, as well as other digital hardware, which may include digital signal processors (DSPs) and dedicated digital logic, and the like. The processing circuitry may be configured to execute program code stored in memory, which may include one or several types of memory, such as read-only memory (ROM), random access memory (RAM), cache memory, flash memory devices, optical storage devices, and the like. The program code stored in memory includes program instructions for executing one or more communication and / or data communication protocols, as well as instructions for implementing one or more of the techniques described herein. In some implementations, the processing circuitry may be used to cause each functional unit to implement a corresponding function according to one or more embodiments of the present disclosure.

[0077] The term unit may have its conventional meaning in the fields of electronics, electrical devices and / or electronic devices and may include, for example, electrical and / or electronic circuitry, devices, modules, processors, memories, logical solid state and / or discrete devices, computer programs or instructions for performing respective tasks, procedures, calculations, output and / or display functions, etc. as described herein.

[0078] In the above description of various embodiments of the inventive concept, it should be understood that the technical terms used herein are merely for describing specific embodiments, and are not intended to limit the inventive concept.Unless otherwise specified, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which the inventive concept belongs.It is further understood that terms as defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of this specification and related technology, and are not to be interpreted in an idealized or overly conventional sense unless so expressly specified herein.

[0079] When an element is referred to as being "connected," "coupled," "responsive to" another element, or variations thereof, the element may be directly connected, coupled, or responsive to the other element, or there may be intervening elements present. In contrast, when an element is referred to as being "directly connected," "directly coupled," "directly responsive" to another element, or variations thereof, there are no intervening elements present. Like numbers refer to like elements throughout. Additionally, as used herein, "coupled," "connected," "responsive," or variations thereof may include wirelessly coupled, connected, or responsive. As used herein, singular terms are intended to include plural terms as well, unless the context clearly indicates otherwise. Well-known features or structures have in some cases not been described in detail for brevity and / or clarity. The term "and / or" includes any and all combinations of one or more of the associated listed items.

[0080] It will be understood that the terms first, second, third, etc. may be used herein to describe various elements / operations, but these elements / operations should not be limited by these terms. These terms are merely used to distinguish one element / operation from another. Thus, a first element / operation in some embodiments may be referred to as a second element / operation in other embodiments without departing from the teachings of the inventive concept. The same reference numerals or the same reference designators refer to the same or similar elements throughout this specification.

[0081] As used herein, "comprises," "comprises," "includes," "included," "has," "having," or variations thereof are open ended and include one or more mentioned features, integers, elements, steps, components, or functions, but do not preclude the presence or addition of one or more other features, integers, elements, steps, components, functions, or groups thereof. Furthermore, as used herein, "for example" may be used to introduce or specify a general example or example of an item already mentioned, but this is not intended to limit such items. "ie" may be used to specify a particular item from a more general description.

[0082] Exemplary embodiments are described herein with reference to block diagrams and / or flowchart illustrations of computer-implemented methods, apparatus (systems and / or devices), and / or computer program products. It is understood that blocks of the block diagrams and / or flowchart illustrations, and combinations of blocks in the block diagrams and / or flowchart illustrations, can be realized by computer program instructions carried out by one or more computer circuits. These computer program instructions can be provided to processor circuitry of general-purpose computer circuits, special-purpose computer circuits, and / or other programmable data processing circuitry such that the instructions, executing via a processor of a computer and / or other programmable data processing apparatus, transform and control transistors, values ​​stored in memory locations, and other hardware components within such circuitry to achieve the functions / acts specified in the block diagrams and / or one or more flowchart blocks, thereby creating the means (functionality) and / or structure for achieving the functions / acts specified in the block diagrams and / or flowchart blocks to produce a machine.

[0083] Also, these computer program instructions may be stored on a tangible computer readable medium that can direct a computer or other programmable data processing apparatus to function in a particular manner to produce an article of manufacture including instructions that implement the functions / acts specified in the block diagrams and / or one or more flowchart blocks, where the instructions stored on the computer readable medium. Thus, embodiments of the inventive concepts may be embodied in hardware and / or in software (including firmware, resident software, microcode, etc.) running on a processor, such as a digital signal processor, which may collectively be referred to as "circuitry," "modules," or variations thereof.

[0084] It should also be noted that in some alternative implementations, the functions / acts noted in the blocks may occur out of the order noted in the flowcharts. For example, two blocks shown in succession may in fact be executed substantially simultaneously, or the blocks may sometimes be executed in the reverse order depending on the functionality / acts involved. Furthermore, the functionality of a given block of the flowcharts and / or block diagrams may be divided into multiple blocks, and / or the functionality of two or more blocks of the flowcharts and / or block diagrams may be at least partially integrated. Finally, other blocks may be added / inserted between the illustrated blocks, and / or blocks / operations may be omitted without departing from the scope of the inventive concept. Furthermore, although some of the diagrams include arrows on communication paths to indicate a primary direction of communication, it should be understood that communication may occur in a direction opposite to that of the depicted arrows.

[0085] Many changes and modifications can be made to the embodiments without substantially departing from the principles of the inventive concept. All such changes and modifications are intended to be included within the scope of the inventive concept herein. The subject matter disclosed above should therefore be considered illustrative rather than restrictive, and the example embodiments are intended to cover all such modifications, improvements, and other embodiments that are within the spirit and scope of the inventive concept. The scope of the inventive concept should therefore be determined by the broadest permissible interpretation of this disclosure, including the example embodiments and their equivalents, to the fullest extent permitted by law, and should not be limited or restricted by the above detailed description. Appendix 1 forms part of this disclosure.

[0086] Appendix 1 [Title of Invention] Method and apparatus for controlling audio frame loss concealment [Technical field]

[0001] The present invention relates to a method and device for controlling a concealment method for lost audio frames of a received audio signal. [Background technology]

[0002] Conventional audio communication systems transmit speech and audio signals frame by frame. The transmitter first arranges the signal into short segments or frames of, for example, 20-40 ms each. These are then coded in sequence and transmitted as logical units, for example in a transmission packet. The receiver decodes each of those logical units and reconstructs the corresponding signal frame. The reconstructed frames are finally output as a continuous sequence of reconstructed signal samples. Prior to encoding, an analog-to-digital (A / D) conversion step is usually performed, which converts an analog speech or audio signal from a microphone into a sequence of audio samples. Conversely, at the receiving end, a final D / A conversion step is usually performed, which converts the reconstructed digital signal samples into a continuous-time analog signal for loudspeaker playback.

[0003] However, in such transmission systems of speech and audio signals, situations can arise whereby transmission errors occur, causing one or some of the transmitted frames to be unavailable for reconstruction at the receiver. The decoder then needs to generate a substitute signal for each lost or unavailable frame. This is performed in a so-called frame loss concealment or error concealment unit of the receiving signal decoder. The objective of frame loss concealment is to make frame losses as inaudible as possible, thereby reducing as much as possible their impact on the quality of the reconstructed signal.

[0004] Conventional frame loss concealment methods rely on the structure or architecture of the codec, for example by repeatedly applying previously received codec parameters. Such parameter repetition techniques obviously depend on the specific parameters of the codec used, and therefore cannot be easily applied to other codecs with different structures. Conventional frame loss concealment methods rely on freezing and extrapolating parameters of previously received frames to generate a replacement frame for a lost frame.

[0005] These prior art frame loss concealment methods include some burst loss handling methods. Typically, when there are several frame losses in a sequence, the composite signal is attenuated until it is completely silenced after a long burst of errors. Furthermore, the coding parameters, which are essentially repeated and extrapolated, are modified to achieve the attenuation and flatten the spectral peaks.

[0006] Conventional frame loss concealment techniques usually apply the concept of freezing and extrapolating parameters of previously received frames to generate a replacement frame for a lost frame. Many parametric speech codecs, such as linear predictive codecs like AMR or AMR-WB, usually freeze or use some extrapolation of previously received parameters and use a decoder with such parameters. In essence, the principle is to set a given model for encoding / decoding and apply the same model with frozen or extrapolated parameters. The frame loss concealment techniques of AMR and AMR-WB can be considered as representative techniques. They are described in detail in the corresponding standard specifications.

[0007] Many of the various audio codecs apply frequency domain coding techniques, where a coding model is applied to the spectral parameters after some frequency domain transformation. The decoder reconstructs the signal spectrum from the received parameters and finally transforms the spectrum back to a time signal. Typically, the time signal is reconstructed frame by frame. Such frames are combined into the final reconstructed signal by overlap-add techniques. Even for that audio codec, conventional error concealment usually applies the same or at least a similar decoding model to the lost frames. The frequency domain parameters from previously received frames are frozen or suitably extrapolated and then used in the frequency-to-time domain transformation. An example of such a technique is provided by the 3GPP audio codec according to the 3GPP standard. [Summary of the Invention]

[0008] Prior art frame loss concealment methods generally suffer from quality deficiencies, the main problem being that parameter freezing, extrapolation techniques and reapplying the same decoder model to the lost frame do not necessarily guarantee a smooth and faithful signal evolution from previously decoded signal frames to the lost frame, so the audible signal is often discontinuous and the quality is affected.

[0009] A novel frame loss concealment scheme for speech and audio transmission systems is described which improves quality in the event of frame loss compared to the quality achievable with conventional frame loss concealment techniques.

[0010] The aim of embodiments of the invention is to control a frame loss concealment scheme, preferably a related novel type of method described below, in such a way that the best possible sound quality of the reconstructed signal is achieved. The embodiment aims to optimise the reconstruction quality with respect to both the signal characteristics and the temporal distribution of the frame losses. A particular problem with frame loss concealment in terms of providing high quality is when the audio signal has highly varying characteristics such as rising and falling energy edges or when the spectrum of the audio signal varies very strongly. In that case the described concealment method repeats said rising and falling edges or spectral variations, resulting in a large deviation from the original signal and a deterioration in quality.

[0011] Another problematic case is when bursts of frame losses occur consecutively. Conceptually, the frame loss concealment method according to the described method deals with such a case, but still introduces tonal artifacts. Another objective of embodiments of the present invention is to reduce such tonal artifacts as much as possible.

[0012] According to a first aspect, a decoder method for concealment of lost audio frames comprises determining characteristics of a previously received and reconstructed audio signal or characteristics of an observed frame loss. The method includes detecting conditions in the statistical characteristics where substitution of a lost frame would result in a relative degradation in quality, and modifying the concealment method if such conditions are detected by selectively adjusting the phase or spectral amplitude of the substitute frame spectrum.

[0013] According to a second aspect, a decoder is configured to provide concealment of a lost audio frame. The decoder comprises a controller for detecting conditions in the characteristics of a previously received reconstructed audio signal or in the statistical characteristics of observed frame losses such that substitution of a lost frame would result in a relative degradation of quality. If such a condition is detected, the controller modifies the concealment method by selectively adjusting the phase or the spectral amplitude of the substitute frame spectrum.

[0014] The decoder may be implemented in a device such as a mobile phone.

[0015] According to a third aspect, a receiver comprises a decoder according to the above-mentioned second aspect.

[0016] According to a fourth aspect, a computer program for concealing a lost audio frame is defined, the computer program comprising instructions which, when executed by a processor, cause the processor to perform concealment of the lost audio frame according to the first aspect above.

[0017] According to a fifth aspect, a computer program product comprises a computer readable medium storing a computer program according to the fourth aspect above.

[0018] An advantage of an embodiment is that an adaptive control of a frame loss concealment method is provided that can significantly reduce the impact of frame losses on the sound quality in the transmission of coded speech and audio signals compared to the quality achieved with conventional concealment methods alone. A general advantage of the embodiment is that a smooth and faithful evolution of the reconstructed signal is provided even for lost frames. The perceptual impact of frame losses is significantly reduced compared to the prior art. [Brief description of the drawings]

[0019] [Figure 1] A diagram showing a rectangular window function. FIG. 2 is a diagram showing a combination of a Hamming window and a rectangular window. FIG. 3 is a diagram showing an example of an amplitude spectrum of a window function. [Figure 4] Frequency f k 4 shows the line spectrum of an exemplary sinusoidal signal of FIG. [Figure 5] Frequency f k 4 shows the spectrum of a windowed sinusoidal signal of FIG. [Figure 6] Diagram showing bars corresponding to the size of the grid points of the DFT based on the analysis frame. [Figure 7] A diagram showing a parabolic fitting through DFT grid points P1, P2, and P3. [Figure 8] A diagram showing fitting of the main lobe of a window spectrum. FIG. 9 is a diagram showing the fitting of a main lobe approximation function P through DFT grid points P1 and P2. FIG. 10 is a flow chart illustrating an exemplary method according to an embodiment of the present invention for controlling a concealment method for lost audio frames of a received audio signal. FIG. 11 is a flowchart illustrating another exemplary method according to an embodiment of the present invention for controlling a concealment method for lost audio frames of a received audio signal. FIG. 12 illustrates another exemplary embodiment of the present invention. FIG. 13 is a diagram showing an example of an apparatus according to an embodiment of the present invention. FIG. 14 is a diagram showing another example of an apparatus according to an embodiment of the present invention. FIG. 15 is a diagram showing another example of an apparatus according to an embodiment of the present invention. [Mode for carrying out the invention]

[0020] The novel control scheme for the described novel frame loss concealment technique includes the following steps as shown in Figure 10. Note that the method can be performed by the decoder's controller.

[0021] 1. Detect (101) conditions in a previously received and reconstructed audio signal or in the statistical characteristics of observed frame losses such that replacement of lost frames would result in poor audio quality in the previously described methods.

[0022] 2. If such a state is detected in step 1, selectively adjust the phase or spectral amplitude to Z(m) = Y(m) e jθ k The method for calculating the alternate frame spectrum is modified (102) by:

[0023] (Sine wave analysis) The first step of the frame loss concealment technique, to which the novel control technique can be applied, involves a sinusoidal analysis of a portion of the previously received signal. The aim of this sinusoidal analysis is to identify the frequency of the main sine wave of the signal. The basic assumption is that the signal is composed of a limited number of individual sine waves, i.e. that it is a multi-sine wave signal of the kind shown below:

[0024] TIFF0007660628000028.tif19170 where K is the number of sinusoids assumed to compose the signal. For each sinusoid, k=1...K, k is the amplitude, f k is the frequency, φ k is the phase. The sampling frequency is f s where the time index of a time-discrete signal sample s(n) is denoted by n.

[0025] It is of primary importance to specify the frequency of the sine wave as accurately as possible. An ideal sine wave signal has a line frequency f k Although it is believed that the signal has a line spectrum of , it would require, in principle, an infinite measurement time to identify its true value. In practice, therefore, it is difficult to discover the line frequency, since it can only be estimated based on a short measurement period corresponding to the signal segment used for the sinusoidal analysis described herein. In the following description, this signal segment is called the analysis frame. Another difficult problem is that the signal is actually a time-varying signal, and the parameters of the above equation vary over time. So, while it is desirable to use a long analysis frame to make the measurement more accurate, it is necessary to shorten the measurement time to better respond to possible signal variations. A suitable trade-off is to use an analysis frame with a length of, for example, about 20-40 ms.

[0026] Frequency of a sine wave, f k A preferred way to make it possible to determine is to perform a frequency domain analysis of the analysis frame. For this purpose, the analysis frame is transformed into the frequency domain, for example by means of a DFT or DCT or a similar frequency domain transformation. If the DFT of the analysis frame is used, the spectrum is represented by

[0027] TIFF0007660628000029.tif16170 where w(n) represents a window function for extracting and weighting an analysis frame of length L. A typical window function is for example a rectangular window which is 1 for n ∈ [0...L-1] as shown in Fig. 1 and 0 otherwise. It is assumed that the time index of the previously received audio signal is set so that the analysis frame is referenced by the time index n = 0...L-1. Other window functions which may be more suitable for spectral analysis are for example the Hamming window, the Hanning window, the Kaiser window or the Blackman window. A window function which has proven to be particularly useful is a combination of a Hamming window and a rectangular window. As shown in Fig. 2, this window has a rising edge shape like the left half of a Hamming window of length L1 and a falling edge shape like the right half of a Hamming window of length L1, and between the rising edge and the falling edge the window is equal to 1 for length L-L1.

[0028] The peak in the amplitude spectrum of the windowed analysis frame |X(m)| is the desired sinusoidal frequency f k However, the accuracy of this approximation is limited by the frequency interval of the DFT. For a DFT of block length L, the accuracy is f s / (2L).

[0029] Experiments have shown that this level of accuracy may be too low within the scope of the methods described herein. Improved accuracy can be obtained based on the following considerations:

[0030] The spectrum of the windowed analysis frame is given by the convolution of the spectrum of the window function with the line spectrum of the sinusoidal model signal S(Ω), followed by sampling at the grid points of the DFT of

[0031] TIFF0007660628000030.tif16170

[0032] Using the spectral representation of the sinusoidal model signal, this can be rewritten as:

[0033] TIFF0007660628000031.tif15170

[0034] The sampled spectrum is then given by:

[0035] TIFF0007660628000032.tif19170 where m=0...L-1

[0036] Based on this idea, the peaks observed in the amplitude spectrum of the analysis frame are transformed into a windowed sinusoidal signal containing K sinusoids around which the true sinusoidal frequencies are identified. The DFT index (grid point) of the observed k-th peak is assumed to be m k Then, the corresponding frequency is TIFF0007660628000033.tif16170, which is the true sine wave frequency f k It can be considered as an approximation of the true sinusoidal frequency f k is the interval It can be assumed that it is located in TIFF0007660628000034.tif19170.

[0037] For clarity, it should be noted that the convolution of the spectrum of the window function with the spectrum of the line spectrum of the sinusoidal model signal can be understood as the superposition of a frequency-shifted version of the window function spectrum, whereby the shift frequency is the frequency of the sinusoid. This superposition is then sampled at the DFT grid points. These steps are illustrated by the following figures: FIG. 3 shows an example of the amplitude spectrum of the window function. FIG. 4 shows the amplitude spectrum (line spectrum) of an example of a sinusoidal signal with a sinusoid of one frequency. FIG. 5 shows the amplitude spectrum of the windowed sinusoidal signal, reproducing and superposing the frequency-shifted window spectrum at the frequency of the sinusoid. The dotted lines in FIG. 6 correspond to the amplitudes of the grid points of the DFT of the windowed sinusoid obtained by computing the DFT of the analysis frame. It should be noted that all spectra are periodic with a normalization frequency parameter Ω, where Ω is the sampling frequency f s , which corresponds to 2π.

[0038] The previous discussion and FIG. 6 suggest that the sinusoidal frequencies can only be better approximated by increasing the resolution of the search compared to the frequency resolution of the frequency domain transform used.

[0039] Frequency of a sine wave, f k One of the preferred ways to find a better approximation of is to apply parabolic interpolation. One such scheme is to do a parabolic fitting through grid points of the DFT amplitude spectrum surrounding the peak and calculate each frequency that belongs to the parabolic maximum. The next best choice for a parabola is 2. In detail, the following procedure can be applied:

[0040] 1. Identify peaks in the DFT of the windowed analysis frame. The peak search outputs the number K of peaks and the corresponding DFT index of the peaks. The peak search can usually be performed on the DFT magnitude spectrum or the logarithmic DFT magnitude spectrum.

[0041] 2. The corresponding DFT index m k For each peak k (k=1…K) with Parabolic fitting is performed through TIFF0007660628000035.tif10170. As a result, the parabolic coefficient b of the parabola defined by the following equation is obtained. k (0), b k (1), b k (2) is obtained.

[0042] TIFF0007660628000036.tif16170This parabolic fitting is shown in Figure 7.

[0043] 3. For each of the K parabolas, the interpolation frequency index corresponding to the value of q at which that parabola has a maximum value Calculate TIFF0007660628000037.tif13170. Sine wave frequency f k As an approximation of Use TIFF0007660628000038.tif16170.

[0044] Although the described approach provides good results, it may have some limitations since a parabola does not approximate the shape of the main lobe of the window function's magnitude spectrum |W(Ω)|. An alternative approach to accomplish this is an improved frequency estimation using a main lobe approximation, as described below. The main idea of ​​this alternative approach is: The function P(q) that approximates the main lobe of TIFF0007660628000039.tif16170 is fitted through the grid points of the DFT amplitude spectrum surrounding the peak, and each frequency that belongs to the function maximum is calculated. The function P(q) is the frequency shift amplitude spectrum of the window function. TIFF0007660628000040.tif17170. However, for numerical simplicity, this should be made a polynomial, which allows easy calculation of the function maximum. The procedure detailed below can be applied.

[0045] 1. Identify the points in the DFT of the windowed analysis frame. The peak search outputs the number of peaks, K, and the corresponding DFT index of the peak. The peak search can usually be performed on the DFT magnitude spectrum or the logarithmic DFT magnitude spectrum.

[0046] 2. Amplitude spectrum of the window function for a given interval (q1, q2) TIFF0007660628000041.tif16170 or Log Magnitude Spectrum Extract the function P(q) that approximates TIFF0007660628000042.tif15170. The selection of the approximation function that approximates the main lobe of the window spectrum is shown in FIG.

[0047] 3. The corresponding DFT index m k For each peak k (k=1…K) with Through the frequency shift function Fitting is performed for TIFF0007660628000043.tif9170. Therefore, |X(m k -1)| is |X(m k +1)|Point if greater Through TIFF0007660628000044.tif10170 Do fitting of TIFF0007660628000045.tif9170, otherwise, point Through TIFF0007660628000046.tif10170 For simplicity, we can choose P(q) to be either a quadratic or a quartic polynomial. This allows the approximation in step 2 to be a simple linear regression calculation and TIFF0007660628000048.tif13170. The interval (q1, q2) can be chosen to be constant and identical for all peaks, e.g. (q1, q2) = (-1, 1), or it can be adaptive. In the adaptive case, the function An interval can be selected to fit the main lobe of the window function spectrum within the range of DFT grid points {P1;P2} associated with TIFF0007660628000049.tif11170. This fitting process is shown in FIG.

[0048] 4. K frequency parameters at which the continuous spectrum of a windowed sinusoidal signal is predicted to have peaks For each of TIFF0007660628000050.tif13170, TIFF0007660628000051.tif16170 to sine wave frequency f k Calculate as an approximation of

[0049] When the transmitted signal is harmonic, it often consists of sine waves with frequencies that are integer multiples of some fundamental frequency f0. This is the case when the signal is highly periodic, for example voiced speech or sustained tones of some musical instruments. The frequencies of the sine wave models of the embodiments are not frequency dependent, but are harmonically related to and originate from the same fundamental frequency. Taking this harmonic property into account can result in a significant improvement in the analysis of the sine wave component frequencies.

[0050] One possible improvement can be outlined as follows:

[0051] 1. Check whether the signal is harmonic or not. This can be done for example by evaluating the periodicity of the signal before the frame loss. One simple way is to perform an autocorrelation analysis of the signal. The maximum of the autocorrelation function for some time delay τ>0 can be used as an indicator. If the value of this maximum exceeds a certain threshold, the signal can be considered to be harmonic. The corresponding time delay τ is then TIFF0007660628000052.tif21170 corresponds to the period of the signal relative to the fundamental frequency.

[0052] Many linear predictive speech coding methods use adaptive codebooks to apply so-called open-loop or closed-loop pitch predictive coding, i.e. CELP coding. When the signal is harmonic, the pitch gain and the associated pitch lag parameter derived by such coding methods are also useful measures, respectively, with respect to the time delay.

[0053] Further methods for obtaining f0 are described below.

[0054] 2. Integer range 1…J max For each harmonic index j in j Check whether there is a peak in the (log) DFT amplitude spectrum of the analysis frame near f = j·f0. j The neighborhood of is where delta is the frequency resolution of the DFT (f s / L) jThe delta range around, i.e. the interval TIFF0007660628000053.tif21170. The corresponding estimated sine wave frequency If a peak with TIFF0007660628000054.tif15170 exists, replace f^k with f^k=j·f0.

[0055] In the above two-step procedure, it is also possible to check whether the signal is harmonic and to extract the shift of the fundamental frequency implicitly, possibly in an iterative manner, without necessarily using indicators from some other method. An example of such a technique is given below.

[0056] Candidate value {f 0,1 …f 0,P} from the set of 0,p Every The number of DFT peaks present in the vicinity of the harmonic frequency, i.e., f, without replacing TIFF0007660628000055.tif16170 0,p Apply step 2 of the procedure while counting integer multiples of f. The fundamental frequency f at which the maximum number of peaks is obtained at or around the harmonic frequency is selected. 0,pmax If the maximum number of peaks exceeds a given threshold, the signal is assumed to be harmonic. Then, f 0,pmax can be assumed to be the fundamental frequency that is used in performing step 2, resulting in the improved sinusoidal frequency f̂k. However, an alternative and more preferred method is to first optimize the fundamental frequency f0 based on the peak frequency f̂k that is known to coincide with a harmonic frequency. The optimization is to find a set of M harmonics, i.e., integer multiples {n1...n} of some fundamental frequency that are known to coincide with some set of M spectral peaks at frequencies f^k(m), m = 1...M. M}, the underlying (optimized) fundamental frequency f 0,opt can be calculated to minimize the error between the harmonic frequency and the spectral peak frequency. The error to be minimized is the mean square error If the image is TIFF0007660628000056.tif18170, the optimal fundamental frequency is The initial set of candidate values ​​is calculated as {f 0,1 …f 0,P} is the frequency of the DFT peak or the estimated sine wave frequency It can be obtained from TIFF0007660628000058.tif15170. Estimated Sine Wave Frequency A further possibility to improve the accuracy of TIFF0007660628000059.tif16170 is to take into account its temporal evolution. For that purpose, the estimates of the sinusoidal frequencies from several analysis frames can be combined, for example by averaging or prediction. Prior to averaging or prediction, peak tracking can be applied, which links each estimated spectral peak to each of the same underlying sinusoids.

[0057] (Applying the sine wave model) In the following, the application of a sinusoidal model for performing frame loss concealment operations is described.

[0058] Consider the case where a given segment of a coded signal cannot be reconstructed by a decoder because the corresponding coding information is unavailable. Furthermore, assume that a portion of the signal past this segment is available. Let y(n), for n=0...N-1, be the unavailable segment for which a substitute frame z(n) has to be generated, and let y(n) for n<0 be the previously decoded available signal. Then, in a first step, a segment of length L and starting index n -1 A prototype frame of the available signal is extracted by a window function w(n) and transformed into the frequency domain, for example by the DFT of

[0059] TIFF0007660628000060.tif23170

[0060] The window function can be one of the window functions described above for the sinusoidal analysis. To reduce numerical complexity, the frames after the frequency domain transformation are preferably the same as those used in the sinusoidal analysis.

[0061] In the next step, an assumed sinusoidal model is applied. According to the assumed sinusoidal model, the DFT of the prototype frame can be written as:

[0062] TIFF0007660628000061.tif16170

[0063] The next step is to realize that the spectrum of the window function used has a significant contribution in the frequency range very close to 0. As shown in Figure 3, the amplitude spectrum of the window function is large for frequencies very close to 0 and small for frequencies that are not (within the normalized frequency range from -π to π, which corresponds to half the sampling frequency). Thus, as an approximation, the window spectrum W(m) is given by min ,m max ](m min and m max We assume that k is non-zero only for small positive integers). In particular, an approximation of the window function spectrum is used such that, for each k, the contributions of the shifted window spectrum in the above formula do not strictly overlap each other. In the above formula, for each frequency index, only the contribution from one summand, i.e. from one shifted window spectrum, is always maximal. This means that the above formula reduces to the following approximation:

[0064] nonnegative m ∈ M k For each k, TIFF0007660628000062.tif19170

[0065] Here, M k is an integer interval TIFF0007660628000063.tif14170, m min,k and m max,k m satisfies the constraints stated above that the intervals must not overlap each other. min,k and m max,kA good choice of is to set them to a small integer value δ, e.g. δ = 3. However, if two adjacent sinusoidal frequencies f k and f k+1 If the DFT index associated with is less than 2δ, then δ should be set to ensure that the intervals do not overlap. The function floor(·) is the integer that is closest to but less than the function argument.

[0066] The next step according to one embodiment is to apply the sinusoidal model according to the above equation and evolve the K sinusoids in time. The time index of the erased segment is n, compared to the time index of the prototype frame. -1 The assumption that the phase of the sine wave differs by only a few samples is This means that the DFT spectrum of the developed sinusoidal model is It is expressed by the following formula.

[0067] TIFF0007660628000066.tif16170

[0068] Applying again the approximation that the shifted window function spectra do not overlap each other, we have a non-negative m ∈ M k For each k, we obtain the following equation:

[0069] TIFF0007660628000067.tif21170

[0070] By using the approximation, the prototype frame Y -1 Comparing the DFT of Y(m) with the DFT of the developed sinusoidal model Y0(m), m∈M k The phase changes every It can be seen that the amplitude spectrum remains unchanged while the sine wave is shifted by 17170. Therefore, the frequency spectrum coefficients of the prototype frames in the vicinity of each sine wave are k and the lossy audio frame and the prototype frame n-1 and the time difference between

[0071] Therefore, according to this embodiment, the alternative frame can be calculated by the following formula: nonnegative m ∈ M k For each k, TIFF0007660628000069.tif13170, TIFF0007660628000070.tif13170

[0072] In one particular embodiment, in any section M k As explained above, we deal with phase randomization for DFT indices that do not belong to the interval M k ,k=1…K must be set so that the intervals do not overlap exactly, which is done using some parameter δ that controls the size of the intervals. It may happen that δ is small relative to the frequency distance of two adjacent sinusoids. Hence, in that case, there may be a gap between the two intervals. So, for the corresponding DFT index m, The phase shift according to TIFF0007660628000071.tif12170 is undefined. A suitable option according to the present embodiment is to randomize the phase for those indices, so that Z(m)=Y(m)·e j2πrand(·) Here, the function rand(·) returns some random number.

[0073] Regarding the quality of the reconstructed signal, k It has been found to be beneficial to optimize the size of . The interval should be large, especially if the signal is very close to a tone signal, i.e. has sharp and well-defined spectral peaks. This is because, for example, the signal has no clear periodicity. In the case where the signal has a wide spectral maximum and is a harmonic with a less clear spectral structure, it has been found that using narrower intervals results in better quality. This discovery leads to a further improvement in that the interval size is adapted according to the characteristics of the signal. One embodiment uses a tonality or periodicity detector. If the detector determines that the signal is close to a tone signal, the δ parameter, which controls the interval size, is set to a relatively large value. Otherwise, the δ parameter is set to a relatively small value.

[0074] Based on the above description, the audio frame loss concealment method includes the following steps.

[0075] 1. Analyze the available segments of previously synthesized signals to find the sinusoidal frequencies f that make up the sinusoidal model, e.g., using the improved frequency estimates. k Get the.

[0076] 2. Find a prototype frame y from the available previously synthesized signal. -1 Then, we extract the vector and compute the DFT of that frame.

[0077] 3. Sine wave frequency f k and the time advance between the prototype frame and the alternative frame, n -1 and the phase shift θ for each sine wave k depending on k In this step, for example, the size of the interval M can be adapted depending on the tonality of the audio signal.

[0078] 4. For each sine wave k, the sine wave frequency f k For DFT indices related to the neighborhood around k Move forward.

[0079] 5. Calculate the inverse DFT of the spectrum obtained in step 4.

[0080] (Analysis and detection of signal and frame loss characteristics) The above method is based on the assumption that the characteristics of the audio signal do not change significantly from the previously received and reconstructed signal frames and lost frames in a short time. In this case, it is a very good choice to keep the amplitude spectrum of the previously reconstructed frames and evolve the phase of the sinusoidal main components detected in the previously reconstructed signals. However, this assumption may be wrong when there are transients, for example with abrupt energy changes or abrupt spectrum changes.

[0081] Therefore, a first embodiment of the transient detector according to the invention can be based on the energy variations of a previously reconstructed signal. This method, illustrated in Fig. 11, calculates the energy of a left and a right part of an analysis frame 113. The analysis frame can be identical to the frame used for the sinusoidal analysis described above. The part of the analysis frame (left or right) can be the first or the last half of the analysis frame or for example the first or the last quarter of the analysis frame 110. The energy calculation of each part is performed by adding the squares of the samples in the partial frames.

[0082] TIFF0007660628000072.tif13170, where y(n) indicates the analysis frame, n left and n right Both are size N part Indicates the start index of a partial frame.

[0083] The energies of the left and right subframes are used to detect signal discontinuities. This is done by calculating the ratio R l / r If R exceeds a threshold (e.g., 10), a discontinuity due to a sudden energy decrease (fall) can be detected (115). Similarly, l / r If falls below another threshold (e.g., 0.1), a discontinuity due to a sudden increase in energy (rising edge) can be detected (117).

[0084] In connection with the above-mentioned concealment method, it has been found that the above-defined energy ratio may be an insensitive measure in many cases. In particular, in the case of real signals, and in particular musical signals, tones of a certain frequency may appear suddenly while other tones of other frequencies may disappear suddenly. The analysis of the signal frames using the above-defined energy ratio may in any case lead to a false detection result for at least one of the tones, since this measure shows a low sensitivity to different frequencies.

[0085] A solution to this problem is described in the following embodiment. First, transient detection is performed in the time-frequency plane. The analysis frame is split (110) into a left partial frame and a right partial frame in a similar manner. However, the two partial frames are separated (after appropriate windowing (111), for example with a Hamming window) by, for example, N part It is transformed into the frequency domain by a point-wise DFT (112).

[0086] TIFF0007660628000074.tif16170 and m=0...N part If -1, TIFF0007660628000075.tif14170

[0087] Now, for each DFT bin of index m, transient detection can be performed frequency selectively. For each DFT index m, the power of the amplitude spectrum of the left and right subframes can be used to calculate the energy ratio (113) as follows:

[0088] TIFF0007660628000076.tif16170

[0089] Experience has shown that frequency selective transient detection with DFT bin resolution is relatively inaccurate due to statistical fluctuations (estimation errors). It has been found that the quality of the calculation is improved if frequency selective transient detection is performed based on frequency bands. k = [m k-1 +1,…,m k ] is m k-1 +1 to mk If we designate a kth interval (k=1...K) that contains DFT bins up to k, then these intervals define K frequency bands. We can then perform frequency group selective transient detection based on the band-wise ratio of each band's energy in the left and right partial frames.

[0090] TIFF0007660628000077.tif16170

[0091] In addition, in section l k = [m k-1 +1,…,m k ] is the frequency band Corresponding to TIFF0007660628000078.tif14170, f s is the audio sampling frequency.

[0092] The lowest lower frequency band boundary m0 can be set to 0, but to mitigate the estimation error that increases with decreasing frequency, the boundary may be set to a DFT index corresponding to a higher frequency. k of TIFF0007660628000079.tif15170, but this is preferably chosen to correspond to the low frequencies where transients still have a significant impact on the hearing effect.

[0093] One suitable choice of the size or width of these frequency bands is to make them equal in size, for example several hundred Hz wide. Another suitable way is to make the width of the frequency bands follow the size of the critical bands of human hearing, i.e. to relate them to the frequency resolution of the auditory system. This is roughly equivalent to making the width of the frequency bands equal for frequencies up to 1 kHz, and then increasing exponentially after about 1 kHz. Exponential increase means, for example, doubling the frequency bandwidth with an increment of the band index k.

[0094] As explained in the first embodiment of the transient detector based on the energy ratio of two partial frames, the ratio related to the band energies or DFT bin energies of the two partial frames is compared to a threshold: an upper threshold is used for the (frequency selective) falling edge detection 115 and a lower threshold is used for the (frequency selective) rising edge detection 117.

[0095] Yet another audio signal dependent indicator suitable for adapting the frame loss concealment method may be based on codec parameters transmitted to the decoder. For example, the codec may be a multimode codec such as ITU-T G.718. Such a codec uses specific codec modes for different types of signals, and a change of codec mode in the frame immediately preceding the frame loss may be considered as a transient indicator.

[0096] Another useful metric for adapting frame loss concealment is the voicing characteristics and codec parameters related to the transmitted signal. Voiced speech refers to highly periodic speech produced by periodic glottal excitation of the human vocal tract.

[0097] A further suitable indicator is an indicator of an estimate of whether the signal content is music or speech. Such an indicator can be obtained from a signal classifier which may usually be part of the codec. If the codec performs such a classification and makes the corresponding classification available as a coding parameter to the decoder, this parameter is preferably used as the signal content indicator used to adapt the frame loss concealment method.

[0098] Another metric that is preferably used to adapt the frame loss concealment method is the burstiness of the frame loss, meaning that several frame losses occur in succession, making it difficult for the frame loss concealment method to use recently decoded useful signal parts for its operation. A prior art metric is the number of consecutively observed frame losses n burstThis counter is incremented by 1 every time a frame loss occurs and is reset to 0 when a valid frame is received. This indicator is used in conjunction with an exemplary embodiment of the present invention.

[0099] (Adaptation of frame loss concealment methods) If the above steps performed indicate a condition suggesting an adaptation of the frame loss concealment operation, the calculation of the spectrum of the alternative frame is modified.

[0100] The initial calculation of the alternate frame spectrum is given by the equation Z(m) = Y(m) e jθ k m) but introduces an adaptation that modifies both the amplitude and the phase. The amplitude is modified by scaling by two factors α(m) and β(m) and the phase is modified by an additional phase component This is corrected by TIFF0007660628000080.tif13170, which causes the alternate frames to be calculated as follows:

[0101] TIFF0007660628000081.tif12170

[0102] In addition, TIFF0007660628000082.tif12170, the original (non-adaptive) frame loss concealment method is used, so their values ​​are their default values.

[0103] The general purpose of introducing amplitude adaptation is to avoid sound artifacts of frame loss concealment methods. Such sound artifacts can be musical sounds, tones or glitches resulting from repetition of transient sounds. Such sound artifacts are believed to lead to quality degradation, and thus avoiding sound artifacts is the objective of the adaptation described here. A suitable method for such adaptation is to modify the amplitude spectrum of the substitute frame to an appropriate extent.

[0104] FIG. 12 illustrates an embodiment of a modified concealment method. burst is the threshold thr burst (For example, thr burst If m exceeds α(m)=3 (121), then preferably amplitude adaptation is performed (123), in which case a damping factor smaller than 1 is used (e.g. α(m)=0.1).

[0105] However, it has been found to be useful to perform an incremental attenuation. A preferred embodiment for achieving this is to define a logarithmic parameter att_per_frame that specifies the logarithmic increase in attenuation per frame. The incremental attenuation rate when the burst counter exceeds a threshold is then calculated as follows:

[0106] TIFF0007660628000083.tif11170 where the constant c is simply a scaling constant that allows the parameter att_per_frame to be specified, for example, in decibels (dB).

[0107] An additional preferred adaptation is performed depending on an indication of the likelihood that the signal is music or speech. In the case of music content, a threshold thr burst It is preferable to increase , and reduce the attenuation per frame. This is equivalent to performing a decreasing adaptation of the frame loss concealment method. The motivation behind this kind of adaptation is that music is generally more susceptible to longer bursts of losses than speech. Therefore, in this case, at least when multiple frame losses are involved, , the original frame loss concealment method, ie the unmodified frame loss concealment method, is still preferred.

[0108] Index R l / r,band (k) or R l / r (m) or R l / rIf a transient is detected based on exceeding a threshold, then a further concealment method adaptation of the amplitude attenuation rate is preferably performed 122. In that case, a suitable adaptation operation 125 is to modify the second amplitude attenuation rate β(m) such that the total attenuation is controlled by the product of two coefficients α(m)·β(m).

[0109] β(m) is set in response to a transient being indicated. If a trailing edge is detected, the coefficient β(m) is preferably selected to reflect the energy reduction of the trailing edge. A suitable option is to set β(m) to the detected gain change, i.e. m∈I k , k=1…K, TIFF0007660628000084.tif14170

[0110] It has been found to be advantageous to limit the energy increase in the alternative frames when a leading edge is detected: in that case, the coefficient can be set to a fixed value (e.g. 1), which means no attenuation or amplification.

[0111] It should be noted that in the above description, the amplitude attenuation factor is preferably applied frequency-selectively, i.e., for each frequency band, with coefficients calculated separately. If the band approach is not used, the corresponding amplitude attenuation factor can be obtained analogically. If frequency-selective transient detection is used at the DFT bin level, β(m) can be set separately for each DFT bin. Alternatively, β(m) can be made globally the same for all m, if no frequency-selective transient indication is used at all.

[0112] A further suitable adaptation of the amplitude decay rate is achieved by introducing an additional phase component in conjunction with the phase modification. This is done by TIFF0007660628000085.tif13170 (127). For a given m, if such a phase correction is used, the attenuation factor β(m) is further reduced. The degree of phase correction is preferably taken into account. If the phase correction is only moderately performed, β(m) is only slightly scaled down, but if the phase correction is strong, β(m) is further scaled down to a greater extent.

[0113] The general purpose of introducing phase adaptation is to avoid generating substitute frames that are too tonal or signal periodic, which would lead to quality degradation. A suitable method for such adaptation is to randomize or dither the phase to an appropriate degree.

[0114] Such phase dithering involves the addition of an additional phase component TIFF0007660628000086.tif13170 is a random value scaled by a control factor This is achieved by setting it to TIFF0007660628000087.tif13170.

[0115] The random value obtained by the function rand(·) is generated, for example, by a pseudorandom number generator, which is assumed to output a single random number in the interval [0,2π].

[0116] The scaling coefficient α(m) in the above equation is the initial phase θ k is dithered. The embodiments presented below address phase adaptation by controlling this scaling factor. Control of the scaling factor is performed similarly to the control of the amplitude modification factor described above.

[0117] According to a first embodiment, the scaling factor α(m) is adapted depending on the burst loss counter n burst is the threshold thr burst (for example burst= 3), a number greater than 0 (e.g., α(m) = 0.2) is used.

[0118] However, it has been found to be beneficial to perform the dithering to an increasingly greater extent. A preferred embodiment for achieving this is to define a parameter dith_increase_per_frame that specifies the dithering increase per frame. Then, when the burst counter exceeds a threshold, the incremental dithering control factor is calculated according to the following formula:

[0119] TIFF0007660628000088.tif9170

[0120] However, in the above equation, α(m) must be limited to a maximum value of 1 for full phase dithering to be achieved.

[0121] Note that the burst loss threshold thr used to initiate phase dithering is burst may be the same threshold as that used for amplitude attenuation, but higher quality can be obtained by setting them individually to optimal values, which in general means that they may be different.

[0122] Depending on the indication of the estimation of whether the signal is music or speech, suitable additional adaptations are performed. In the case of music content, a threshold thr burst It is preferable to increase . This means that, compared to speech, phase dithering for music is performed only if there is a large number of consecutive lost frames. This is equivalent to performing a decreasing adaptation of the frame loss concealment method for music. The reason behind this kind of adaptation is that music is generally less susceptible to long loss bursts than speech. Therefore, in this case, at least for a large number of consecutive frame losses, the original frame loss concealment method, i.e. the unmodified frame loss concealment method, is still preferred.

[0123] A further preferred embodiment is to adapt the phase dithering depending on the detected transient, in which case a stronger degree of phase dithering can be used for the DFT bin m where a transient is indicated with respect to that bin, the DFT bins of the corresponding frequency band, or the DFT bins of the entire frame.

[0124] Part of the described scheme deals with optimizing frame loss concealment methods for harmonic signals, particularly voiced harmonic signals.

[0125] If the method using improved frequency estimation as described above is not implemented, another possible application of the quality optimized frame loss concealment method for voiced speech signals is to switch to another frame loss concealment method specifically designed and optimized for speech, rather than a method for general audio signals including music and speech, in which case an indication that the signal contains a voiced speech signal is used to select another speech optimized frame loss concealment method than the one described above.

[0126] The embodiment is applied in a controller of a decoder as shown in Fig. 13. Fig. 13 is a schematic block diagram of a decoder according to the embodiment. The decoder 130 comprises an input unit 132 arranged to receive an encoded audio signal. The figure shows frame loss concealment by a logical frame loss concealment unit 134, which indicates that the decoder is arranged to realise concealment of lost audio frames according to the previously described embodiment. The decoder further comprises a controller 136 for realising the previously described embodiment. The controller 136 is arranged to detect a condition in the characteristics of the received reconstructed audio signal or in the statistical characteristics of the observed frame losses such that replacement of lost frames according to the previously described method would relatively degrade the quality. If such a condition is detected, the controller 136 selectively adjusts the phase or the spectral amplitude to achieve a higher quality. 14. The detection may be performed by a detector unit 146 and the correction may be performed by a corrector unit 148 as shown in FIG.

[0127] The decoder, with the units contained therein, can be implemented in hardware. There are many variations in the circuit elements that can be used and combined to implement the functions of the units of the decoder. Such variations are included in the embodiments. Particular examples of hardware implementations of the decoder are digital signal processor (DSP) hardware and integrated circuit technologies, both of which include general purpose electronic circuits and application specific circuits.

[0128] Alternatively, for reconstructing an audio signal, including performing audio frame loss concealment according to an embodiment described herein as shown in Fig. 13, a decoder 150 as described herein may be implemented, for example, as shown in Fig. 15, i.e. by one or more of a processor 154 and suitable software 155 with suitable storage or memory 156. An incoming encoded audio signal is received by an input terminal (IN) 152, to which a processor 154 and a memory 156 are connected. A decoded, reconstructed audio signal obtained from the software is output at an output terminal (OUT) 158.

[0129] The techniques described above may be used, for example, in receivers usable in mobile equipment (eg mobile phones, laptops) or fixed devices such as personal computers.

[0130] It will be understood that the selection of interacting units or modules, as well as the names of those units, are merely examples and may be configured in multiple alternative ways to enable the disclosed processing operations to be performed.

[0131] It should be noted that the units or modules described in this specification should be considered as logical entities, not necessarily separate physical entities. The scope of the technology disclosed in this specification includes all other embodiments that are considered obvious to those skilled in the art. It will be understood that the scope of the disclosure herein should not be limited accordingly.

[0132] When describing an element in the singular, it does not mean "only one" element, but rather "one or more" elements, unless expressly indicated otherwise. All structures and functions known to those of ordinary skill in the art that are equivalent to the elements of the previously described embodiments are expressly incorporated herein by reference and are intended to be included in the present invention. Moreover, it is not necessary for a device or method to address each and every problem sought to be solved by the technology disclosed herein, to be included in the present invention.

[0133] In the above description, specific details such as specific structures, interfaces, techniques, etc. are described for the convenience of explanation in order to fully understand the disclosed technology, but are not intended to limit the present invention. However, it will be apparent to those skilled in the art that the disclosed technology may be implemented in other embodiments and / or combinations of embodiments that deviate from those specific details. That is, those skilled in the art will be able to devise various configurations that embody the principles of the disclosed technology, although not explicitly described or shown in the present specification. In some cases, detailed descriptions of well-known devices, circuits, and methods have been omitted so as not to obscure the description of the disclosed technology with unnecessary details. All statements herein that describe the principles, aspects, and embodiments of the disclosed technology, as well as specific examples thereof, are intended to include both equivalent structures and equivalent functions. Moreover, such equivalents are intended to include equivalents developed in the future in addition to currently known equivalents, for example, any elements developed to perform the same function regardless of structure.

[0134] Thus, for example, those skilled in the art will appreciate that the accompanying figures may be substantially represented in terms of illustrative circuits or other functional units and / or computer-readable media embodying principles of the technology, and may represent conceptual diagrams of various operations that may be performed by a computer or processor, although not explicitly shown in the figures.

[0135] The functions of the various elements including the functional blocks may be provided through the use of circuit hardware and / or hardware capable of executing software in the form of coded instructions stored on a computer-readable medium. Thus, such functions and the illustrated functional blocks should be understood to be hardware-implemented and / or computer-implemented and thus machine-implemented.

[0136] The above-described embodiments should be understood as some illustrative examples of the present invention. It will be understood by those skilled in the art that various modifications, combinations and changes may be made to those embodiments without departing from the scope of the present invention. In particular, the methods of different parts of different embodiments may be combined in other configurations if technically possible.

Claims

1. 1. A method for controlling a concealment method performed by an audio decoder for a lost audio frame associated with a received audio signal, comprising: Decoding (901) a first audio frame of the received audio signal to obtain Modified Discrete Cosine Transform (MDCT) coefficients; determining (307-309, 903) a value of a first spectral shape based on the decoded MDCT coefficients obtained for the first acoustic frame, the first spectral shape comprising a number of sub-bands; decoding (907) a second audio frame of the received audio signal subsequent to the first audio frame to obtain MDCT coefficients for a second audio frame preceding the lost audio frame; determining (307-309, 913) a value of a second spectral shape based on the decoded MDCT coefficients obtained from the second acoustic frame, the second spectral shape including the number of sub-bands; Transforming the first spectral shape values ​​and first frame energies of the first acoustic frame into a first representation of a first Fast Fourier Transform (FFT) based spectral analysis (917), and transforming the second spectral shape values ​​and second frame energies of the second acoustic frame into a second representation of a second FFT based spectral analysis (917); Detecting a transient based on the first representation of the first FFT and the second representation of the second FFT (919); modifying (921) the concealment method by selectively adjusting a spectral magnitude of a substitute frame spectrum in response to detecting the transient; The method includes:

2. determining the value of the first spectral shape based on the MDCT coefficients; determining (307) the overall magnitude of the MDCT coefficients; normalizing individual subband values ​​of the first spectral shape; storing each normalized subband value as one of the values ​​of the first spectral shape; The method of claim 1 , comprising:

3. The overall magnitude of the MDCT coefficients is where shape_tot is the total magnitude of the MDCT coefficients, and N ＭＤＣＴ 3. The method of claim 2, wherein q_d(n) is a number of MDCT coefficients depending on the sampling frequency, and q_d(n) is said MDCT coefficient.

4. Normalizing the individual subbands where shape ｏｌｄ where n is the spectral shape of subband(k), shape_tot is the total magnitude of the MDCT coefficients, q_d(n) is the MDCT coefficients, grp_bin(k) is the starting index for the MDCT coefficients in subband(k), and N ｇｒｐ The method according to claim 2 or 3, wherein: is the number of subbands.

5. 5. The method of claim 1, wherein converting the values ​​of the first spectral shape and the first frame energy to the first representation of a first FFT-based spectral analysis and converting the values ​​of the second spectral shape and the second frame energy to a second representation of a second FFT-based spectral analysis comprises applying a conversion factor to the values ​​of the first spectral shape and the first frame energy, and the values ​​of the second spectral shape and the second frame energy.

6. The method of claim 5 , wherein the conversion factor depends on a sampling frequency.

7. E ｏｏｌｄ (k) = m・shape ｏｏｌｄ (k)・E_w ｏｏｌｄ ,0≦+<N ｇｒｐ and and converting the values ​​of the second spectral shape and the second frame energy to a second representation of a second FFT-based spectral analysis according to Here, E ｏｏｌｄ (k) is the first representation, μ is a transformation factor, and shape ｏｏｌｄ (k) is the spectral shape of subband (k) of the first spectral shape, and E_w ｏｏｌｄ is the first frame energy, E ｏｌｄ (k) is the second representation, shape ｏｌｄ (k) is the spectral shape of subband (k) of the second spectral shape, and E_w ｏｌｄ is the second frame energy, and N ｇｒｐ The method according to claim 4 , wherein m is the number of subbands.

8. E ｏｏｌｄ (k) and E ｏｌｄ determining 1105 whether a ratio between the respective band energies of the frames associated with (k) is greater than a threshold; modifying the concealment method by selectively adjusting (1107) the spectral magnitude of the substitute frame spectrum in response to the ratio being greater than the threshold. The method of claim 7 further comprising:

9. The permuted frame spectrum and adjusting the magnitude of the spectrum includes adjusting β(m) (1107), where Z(m) is the substitute frame spectrum, α(m) is a first magnitude attenuation factor, β(m) is a second magnitude attenuation factor, Y(m) is a prototype frame, and θ ｋ 9. The method of claim 8, wherein: is a phase shift and θ(m) is an additive phase component.

10. The determined value of the first spectral shape is called shape ｏｌｄ storing in a buffer; Determining the first frame energy of the first acoustic frame (905), and denoting the determined first frame energy as E_w ｏｌｄ storing in a buffer; In response to decoding the second acoustic frame, the determined value of the first spectral shape is ｏｌｄ Shape from buffer ｏｏｌｄ Moving to a buffer (303, 909); The determined first frame energy is expressed as E_w ｏｌｄ E_w from the buffer ｏｏｌｄ Moving to a buffer (305, 911); The determined value of the second spectral shape is called shape ｏｌｄ storing in a buffer; determining (915) the second frame energy of the second acoustic frame, and comparing the determined second frame energy with E_w ｏｌｄ Storing in a buffer The method of claim 1 , further comprising:

11. The first frame energy and the second frame energy each have a frame energy of: E_w ｏｏｌｄ =E_w ｏｌｄ where E_w ｏｏｌｄ is the preceding frame energy, and E_w ｏｌｄ is the frame energy of the first frame being calculated or the second frame being calculated, and w ｗｈｒ is the long FFT spectrum analysis window, and x ｐｒｅｖ is the time-domain prototype signal used to generate a replacement for a potentially lost frame, and L ｐｒｏｔ is the x ｐｒｅｖ The method of claim 1 , wherein the number of samples in the signal is the number of samples in the signal.

12. Receiving a bad frame indicator (403, 501); In response to receiving the bad frame indicator, ｏｏｌｄ Buffer and the E_w ｏｏｌｄ Flushing the energy buffer; and receiving a new audio frame of the received audio signal; determining (503) a new spectral shape value based on the decoded MDCT coefficients from the decoded new audio frame; and calculating the calculated new spectral shape value based on the shape ｏｌｄ Buffer and the shape ｏｏｌｄ storing (405) in a buffer a value of a new spectral shape, the new spectral shape including a number of sub-bands; determining (505) a new frame energy of the acoustic frame, and calculating the calculated new frame energy by E_w ｏｌｄ Buffer and the E_w ｏｏｌｄ storing in a buffer (407); The method of claim 10 or 11, further comprising:

13. An apparatus (1201, 1301) configured to control a concealment method for a lost audio frame associated with a received audio signal, comprising: decoding a first audio frame of the received audio signal to obtain modified discrete cosine transform (MDCT) coefficients; determining a first spectral shape value based on the decoded MDCT coefficients obtained for the first acoustic frame, the first spectral shape comprising a number of subbands; decoding a second audio frame of the received audio signal subsequent to the first audio frame to obtain MDCT coefficients for a second audio frame preceding the lost audio frame; determining a second spectral shape value based on the decoded MDCT coefficients obtained for the second acoustic frame, the second spectral shape comprising the number of sub-bands; converting the first spectral shape values ​​and first frame energies of the first acoustic frame into a first representation of a first Fast Fourier Transform (FFT) based spectral analysis and converting the second spectral shape values ​​and second frame energies of the second acoustic frame into a second representation of a second FFT based spectral analysis; Detecting a transient based on the first representation of the first FFT and the second representation of the second FFT; and In response to detecting the transient, modifying the concealment method by selectively adjusting the spectral magnitude of the substitute frame spectrum. The device is set up as follows.

14. 14. Apparatus according to claim 13, wherein the apparatus is configured to perform the operations according to any one of claims 2 to 12.

15. 15. Apparatus according to claim 13 or 14, wherein the apparatus is an audio decoder.

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