Noise reduction method and device, earphone device, storage medium and chip

Abstract

The present disclosure relates to a noise reduction method and device, earphone equipment, storage medium and chip, and relates to the field of sound signals. The method comprises: performing short-time Fourier transform processing on an audio signal to be processed to obtain a target audio signal, determining filter coefficients corresponding to the target audio signal, determining sound pressure level information and sub-band energy proportion of the target audio signal according to the filter coefficients and the target audio signal, adjusting a noise reduction notch level corresponding to an environment of the earphone equipment according to the sub-band energy proportion and the sound pressure level information, and finally performing noise reduction on the target audio signal according to the noise reduction notch level. The notch level can be dynamically adjusted to achieve the noise reduction function without pre-setting multiple template coefficients, and the instability and inaccuracy of extracting feature values from environmental acoustic signals are avoided. The stability and accuracy of the noise reduction effect can be improved with less computing resource occupation and lower hardware cost.

Description

Technical Field

[0001] This disclosure relates to the field of audio signals, and more particularly to a noise reduction method, apparatus, headphone device, and storage medium. Background Technology

[0002] Noise-canceling headphones provide a comfortable listening experience for people in their daily work and life. Currently, mainstream noise-canceling headphones generally achieve active noise cancellation through environmental adaptive algorithms. In related technologies, noise cancellation is usually achieved through pattern matching algorithms. Although this method achieves environmental adaptation to a certain extent, a certain number of preset template coefficients need to be set to ensure the effect. Different template coefficients correspond to different preset modes. However, setting multiple template coefficients will lead to higher hardware costs. In addition, it is difficult to extract effective feature values ​​from unstable acoustic signals in daily environments, resulting in lower accuracy and stability of pattern matching algorithms. Summary of the Invention

[0003] To overcome the problems existing in the related technologies, this disclosure provides a noise reduction method, apparatus, headphone device and storage medium.

[0004] According to a first aspect of the present disclosure, a noise reduction method is provided, applied to an earphone device, the method comprising:

[0005] The target audio signal is obtained by performing a short-time Fourier transform on the audio signal to be processed.

[0006] Determine the filter coefficients corresponding to the target audio signal;

[0007] Based on the filter coefficients and the target audio signal, determine the sound pressure level information and subband energy ratio of the target audio signal;

[0008] Based on the subband energy ratio and the sound pressure level information, adjust the noise cancellation level of the headphone device in the corresponding environment;

[0009] The target audio signal is denoised according to the noise reduction level.

[0010] Optionally, the filter coefficients are A-weighted filter coefficients, and determining the sound pressure level information and sub-band energy ratio of the target audio signal based on the filter coefficients and the target audio signal includes:

[0011] The A-weighted sound pressure level of the target audio signal is determined based on the A-weighted filter coefficients and the target audio signal.

[0012] Based on the target audio signal, determine the sub-band energy percentage of the target audio signal.

[0013] Optionally, determining the A-weighted sound pressure level of the target audio signal based on the A-weighted filter coefficients and the target audio signal includes:

[0014] The first data of the i-th frequency point in the k-th frame of the target audio signal is multiplied by the A-weighted filter coefficients of the i-th frequency point in the k-th frame to obtain the second data of the i-th frequency point in the k-th frame; where k and i are positive integers and their initial values ​​are 1.

[0015] After setting i = i + 1, the step of performing the dot product operation on the first data of the i-th frequency point of the k-th frame in the target audio signal and the A-weighted filter coefficient of the i-th frequency point of the k-th frame to obtain the second data of the i-th frequency point of the k-th frame is executed again until the second data of each frequency point of the k-th frame is obtained.

[0016] After obtaining the second data of each frequency point of each frame in the target audio signal, the A-weighted sound pressure level of the target audio signal is obtained by performing calculations based on the second data of each frequency point of each frame.

[0017] Optionally, determining the sub-band energy percentage of the target audio signal based on the target audio signal includes:

[0018] The first value is obtained by summing the squares of the first data points of each frequency point of the first sub-band of the k-th frame of the target audio signal.

[0019] The second value is obtained by summing the squares of the first data points of each frequency point of the second sub-band of the k-th frame of the target audio signal;

[0020] The ratio of the first value to the second value is obtained as the sub-band energy ratio of the k-th frame; the k-th frame is any frame in the target audio signal.

[0021] Optionally, the sound pressure level information is an A-weighted sound pressure level, and adjusting the noise cancellation level of the headphone device in the given environment based on the sub-band energy ratio and the sound pressure level information includes:

[0022] Based on the first preset gear threshold, the second preset gear threshold, the sub-band energy ratio, and the set gap value, the adjusted first actual gear threshold and the second actual gear threshold are determined.

[0023] Based on the first actual level threshold, the second actual level threshold, and the A-weighted sound pressure level, the noise cancellation level of the headphone device is adjusted to the corresponding level in the environment.

[0024] Optionally, determining the adjusted first actual gear threshold and second actual gear threshold based on the first preset gear threshold, the second preset gear threshold, the sub-band energy ratio, and the set gap value includes:

[0025] Subtract the product of the first preset gear threshold and the sub-band energy ratio and the gap value to obtain the first actual gear threshold;

[0026] The second preset gear threshold is added to the product of the sub-band energy ratio and the gap value to obtain the second actual gear threshold.

[0027] Optionally, adjusting the noise cancellation level of the headphone device in the given environment based on the first actual noise cancellation threshold, the second actual noise cancellation threshold, and the A-weighted sound pressure level includes:

[0028] If the A-weighted sound pressure level is greater than the second actual gear threshold, the gear level will be switched from the first level to the second level.

[0029] If the A-weighted sound pressure level is less than the first actual gear threshold, the noise reduction gear level is switched from the second level to the first level.

[0030] Optionally, the filter coefficients are A-weighted filter coefficients, and determining the filter coefficients corresponding to the target audio signal includes:

[0031] Based on data on human auditory perception characteristics and the frequency of the target audio signal, the A-weighted filter coefficients are obtained using the A-weighted filter coefficient calculation formula.

[0032] Optionally, the method further includes:

[0033] Acquire sound signals in the current environment;

[0034] The sound signal is processed by frame segmentation to obtain the audio signal to be processed.

[0035] According to a second aspect of the present disclosure, a noise reduction device is provided for use in an earphone device, the device comprising:

[0036] The processing module is configured to perform a short-time Fourier transform on the audio signal to be processed to obtain the target audio signal.

[0037] The first determining module is configured to determine the filter coefficients corresponding to the target audio signal;

[0038] The second determining module is configured to determine the sound pressure level information and subband energy ratio of the target audio signal based on the filter coefficients and the target audio signal.

[0039] The adjustment module is configured to adjust the noise cancellation level of the headphone device in the given environment based on the subband energy ratio and the sound pressure level information.

[0040] The noise reduction module is configured to reduce noise in the target audio signal according to the noise reduction level.

[0041] According to a third aspect of the present disclosure, a headphone device is provided, comprising:

[0042] processor;

[0043] Memory used to store processor-executable instructions;

[0044] The processor is configured to execute the executable instructions to implement the noise reduction method described in any one of the first aspects of this disclosure.

[0045] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided that stores computer program instructions thereon, which, when executed by a processor, implement the steps of the method described in any of the first aspects of the present disclosure.

[0046] According to a fifth aspect of the present disclosure, a chip is provided, including a processor and an interface; the processor is configured to read instructions to perform the steps of the method described in any of the first aspects of the present disclosure.

[0047] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects:

[0048] In the above technical solution, the audio signal to be processed is subjected to a short-time Fourier transform to obtain the target audio signal. Then, the filter coefficients corresponding to the target audio signal are determined. Next, based on the filter coefficients and the target audio signal, the sound pressure level (SPL) and subband energy ratio of the target audio signal are determined. Then, based on the subband energy ratio and the SPL information, the noise reduction level of the headphone device is adjusted according to the environment. Finally, noise reduction is performed on the target audio signal based on the noise reduction level. This technical solution dynamically adjusts the noise reduction level based on the subband energy ratio and the SPL information to achieve noise reduction. This method does not require pre-setting multiple template coefficients and avoids the instability and inaccuracy problems of extracting feature values ​​from environmental acoustic signals. Therefore, it can improve the stability and accuracy of noise reduction while consuming fewer computational resources and reducing hardware costs.

[0049] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0050] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0051] Figure 1 This is a schematic diagram of an application scenario involved in various embodiments of this disclosure.

[0052] Figure 2 This is a flowchart illustrating a noise reduction method according to an exemplary embodiment.

[0053] Figure 3 This is a flowchart illustrating a noise reduction method according to an exemplary embodiment.

[0054] Figure 4 This is a flowchart illustrating a noise reduction method according to an exemplary embodiment.

[0055] Figure 5 This is a flowchart illustrating a noise reduction method according to an exemplary embodiment.

[0056] Figure 6 This is a flowchart illustrating a noise reduction method according to an exemplary embodiment.

[0057] Figure 7 This is a schematic diagram illustrating the adjustment of noise reduction level according to an exemplary embodiment.

[0058] Figure 8 This is a block diagram illustrating a noise reduction device 800 according to an exemplary embodiment.

[0059] Figure 9 This is a block diagram illustrating a headphone device 900 according to an exemplary embodiment. Detailed Implementation

[0060] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0061] It should be noted that all actions involving the acquisition of signals, information, or data in this application are carried out in compliance with the relevant data protection laws and policies of the country where the application is located, and with the authorization granted by the owner of the relevant device.

[0062] Before introducing the noise reduction method provided in this disclosure, the application scenarios involved in this disclosure will be introduced first. Figure 1 This is a schematic diagram illustrating an application scenario involved in various embodiments of this disclosure. See also... Figure 1 The feedforward microphone on the headphone device can be used to collect ambient noise in real time. Then, the noise signal is filtered by the noise reduction method provided in this embodiment of the present disclosure, and the filtered signal is processed in reverse and broadcast through the speaker, thereby achieving the effect of noise signal superposition and cancellation, and finally realizing the noise reduction function of the headphone device.

[0063] Figure 2 A flowchart illustrating a noise reduction method according to an exemplary embodiment is shown, such as... Figure 2 As shown, this noise reduction method is applied to headphone devices and includes the following steps.

[0064] In step S21, the audio signal to be processed is subjected to short-time Fourier transform to obtain the target audio signal.

[0065] The audio signal to be processed can be an ambient sound signal acquired in real time by the headphone device through a feedforward microphone, and obtained by framing and windowing the ambient sound signal. In other words, after the headphone device acquires the ambient sound signal in real time through the feedforward microphone, it performs framing and windowing on the ambient sound signal, and then performs a Short-Time Fourier Transform (STFT) on the framed and windowed audio signal to obtain the target audio signal. The Short-Time Fourier Transform (STFT), also known as the short-time spectrum, can be understood as dividing a longer time signal into shorter segments of equal length and performing a Fourier transform on each shorter segment.

[0066] In step S22, the filter coefficients corresponding to the target audio signal are determined.

[0067] The filter coefficients can be A-weighting filter coefficients. A-weighting is widely used in environmental noise measurement and is suitable for describing human hearing. Weighting, also known as weighting or hearing compensation, refers to the artificial correction added to the measured value considering the different conditions of normal use and measurement. It can also be understood as a correction coefficient added to the measurement to accurately reflect the measured object. When measuring noise, since the human ear is most sensitive to 1-5kHz and insensitive to low-frequency components, it is necessary to weight each part of the audio spectrum when evaluating the noise level audibly. For example, when measuring noise, it is necessary to pass the noise through a filter equivalent to the frequency characteristics of hearing to reflect the sensitivity of the human ear at different frequencies. To simulate the different sensitivities of human hearing at different frequencies, a network is designed that can simulate the auditory characteristics of the human ear and correct the electrical signal to approximate hearing. This network is called a weighting network. The sound pressure level measured by the weighting network can be understood as the sound pressure level after auditory correction, and can be called the weighted sound level. A-weighting simulates the human ear's response to sound, resulting in significant attenuation of the mid- and low-frequency ranges of electrical signals. Typically, A-weighted filters cover a frequency range of 20Hz to 20kHz, and measurements using A-weighting are usually labeled in dB(A).

[0068] In step S23, the sound pressure level information and subband energy ratio of the target audio signal are determined based on the filter coefficients and the target audio signal.

[0069] It is understandable that this sound pressure level (SPL) can be an A-weighted sound pressure level.

[0070] In step S24, the noise reduction level of the headphone device is adjusted according to the subband energy ratio and the sound pressure level information in the environment.

[0071] Currently, noise-canceling headphones typically have multiple noise-canceling levels, each with a pre-set noise-canceling scheme to handle different noise environments (or scenarios). When performing noise cancellation, the headphones need to determine the appropriate noise-canceling level for the given environment. Once the level is determined, the target audio signal is denoised according to the corresponding noise-canceling scheme, thus eliminating ambient noise. It is understood that the number of noise-canceling levels can be adjusted according to the application scenario, and this disclosure does not limit this number.

[0072] In step S25, the target audio signal is denoised according to the noise reduction level.

[0073] It is understandable that by determining the actual gear level corresponding to the target audio signal, environmental adaptive noise reduction can be achieved through anti-phase noise cancellation.

[0074] In the above technical solution, the audio signal to be processed is subjected to a short-time Fourier transform to obtain the target audio signal. Then, the filter coefficients corresponding to the target audio signal are determined. Next, based on the filter coefficients and the target audio signal, the sound pressure level (SPL) and subband energy ratio of the target audio signal are determined. Then, based on the subband energy ratio and the SPL information, the noise reduction level of the headphone device is adjusted according to the environment. Finally, noise reduction is performed on the target audio signal based on the noise reduction level. This technical solution dynamically adjusts the noise reduction level based on the subband energy ratio and the SPL information to achieve noise reduction. This method does not require pre-setting multiple template coefficients and avoids the instability and inaccuracy problems of extracting feature values ​​from environmental acoustic signals. Therefore, it can improve the stability and accuracy of noise reduction while consuming fewer computational resources and reducing hardware costs.

[0075] Figure 3 This is a flowchart illustrating a noise reduction method according to an exemplary embodiment, such as... Figure 3 As shown, before obtaining the target audio signal after performing a short-time Fourier transform on the audio signal to be processed in step S21, the noise reduction method may further include:

[0076] Step S26: Acquire sound signals in the current environment;

[0077] Step S27: Perform frame segmentation on the sound signal to obtain the audio signal to be processed.

[0078] For example, the audio signal of the user's current environment can be acquired in real time through the feedforward microphone of the headphone device. The audio signal can then be preprocessed, which may include frame processing. For example, the frame length can be set to 512 sampling points (32ms) and the overlap length can be set to 256 sampling points (16ms) to obtain the audio signal to be processed.

[0079] Optionally, after performing a short-time Fourier transform on the audio signal to be processed in step S21, the target audio signal is obtained. The Fourier transform can be calculated using the following formula:

[0080]

[0081] Where x[n] represents the time-domain audio signal after frame division, ω[nm] represents the window function, and ω represents the angular frequency.

[0082] Optionally, the filter coefficients can be A-weighted filter coefficients, and correspondingly, step S22, which determines the filter coefficients corresponding to the target audio signal, may include:

[0083] By combining data on human hearing perception characteristics and the frequency of the target audio signal, the A-weighted filter coefficients are obtained using the A-weighted filter coefficient calculation formula.

[0084] For example, the A-weighted filter coefficients R can be generated according to the following formula. A (f):

[0085]

[0086] In the formula, f represents the frequency of each frame after framing. The A-weighted filter coefficients R corresponding to each frame can be obtained through this formula. A (f) In determining the coefficients of the A-weighted filter, it is necessary to combine data on human ear perception characteristics, and strengthen the target audio signal by enhancing the sensitive areas of the human ear and weakening the non-sensitive areas. This data on human ear perception characteristics can be obtained through experimental or empirical data.

[0087] Figure 4 This is a flowchart illustrating a noise reduction method according to an exemplary embodiment, such as... Figure 4 As shown, step S23, which involves determining the sound pressure level information and sub-band energy ratio of the target audio signal based on the filter coefficients and the target audio signal, may include the following steps:

[0088] Step S231: Determine the A-weighted sound pressure level of the target audio signal based on the A-weighted filter coefficients and the target audio signal.

[0089] For example, the A-weighted sound pressure level spl(A) of the target audio signal can be obtained using the following two formulas:

[0090] y(k,i)=X(k,i)⊙A(k,i)

[0091]

[0092] In the formula, X(k,i) represents the i-th frequency point data of the k-th frame after Fourier transform, A(k,i) represents the A-weighted filter coefficient of the i-th frequency point of the k-th frame, and ⊙ represents the dot product operation, for example, the Hadamard product operation.

[0093] As shown in the formula above, the following steps can be performed:

[0094] (1) First, perform a dot product operation on the first data of the i-th frequency point of the k-th frame in the target audio signal and the A-weighted filter coefficient of the i-th frequency point of the k-th frame to obtain the second data of the i-th frequency point of the k-th frame; where k and i are positive integers and the initial value of k and i is 1;

[0095] (2) After setting i = i + 1, perform the step of performing a dot product operation on the first data of the i-th frequency point of the k-th frame of the target audio signal and the A-weighted filter coefficient of the i-th frequency point of the k-th frame to obtain the second data of the i-th frequency point of the k-th frame again, until the second data of each frequency point of the k-th frame is obtained; after the second data of each frequency point of the k-th frame is obtained, set k = k + 1 and repeat the above steps (1) and (2). By performing the above steps on each frame of the target audio signal, the second data of each frequency point of each frame of the target audio signal can be obtained.

[0096] After obtaining the second data of each frequency point of each frame in the target audio signal, the A-weighted sound pressure level spl(A) of the target audio signal is obtained by performing calculations based on the second data of each frequency point of each frame.

[0097] Step S232: Determine the sub-band energy ratio of the target audio signal based on the target audio signal.

[0098] For example, the sum of squares of the first data points of each frequency point of the first sub-band of the k-th frame of the target audio signal can be obtained to get the first value;

[0099] Then, obtain the sum of squares of the first data points of each frequency point of the second sub-band of the k-th frame of the target audio signal to get the second value;

[0100] Finally, the ratio of the first value to the second value is obtained as the sub-band energy proportion α of the k-th frame. The k-th frame is any frame in the target audio signal, and each frame includes two sub-bands. For the k-th frame, the first sub-band is the first sub-band in the k-th frame, and the second sub-band is the second sub-band in the k-th frame. The above steps can be calculated according to the following formula to obtain the sub-band energy proportion α:

[0101]

[0102] In the formula, X(k,i) represents the i-th frequency point data of the k-th frame after Fourier transform, m1 and n1 represent the start and end frequency points of the first sub-band of the k-th frame, respectively, and m2 and n2 represent the start and end frequency points of the second sub-band of the k-th frame, respectively.

[0103] Figure 5This is a flowchart illustrating a noise reduction method according to an exemplary embodiment, such as... Figure 5 As shown, step S24, which involves adjusting the noise cancellation level of the headphone device based on the sub-band energy ratio and the sound pressure level information, may include the following steps:

[0104] Step S241: Determine the adjusted first actual gear threshold and second actual gear threshold based on the first preset gear threshold, the second preset gear threshold, the energy ratio of the sub-band, and the set gap value.

[0105] For example, the adjusted first actual gear threshold T1′ and second actual gear threshold T2′ can be determined according to the following formula:

[0106] T1′=T1-α*G

[0107] T2′=T2+α*G

[0108] In the formula, T1 and T2 represent the first preset gear threshold and the second preset gear threshold, respectively, which can be set according to the actual environment corresponding to each gear; G is the set gap value, which can be set according to the application scenario; and α represents the sub-band energy ratio obtained above.

[0109] As shown in the above formula, the first preset gear threshold is first subtracted from the product of the sub-band energy ratio and the gap value to obtain the first actual gear threshold.

[0110] Then, the second preset gear threshold is added to the product of the sub-band energy ratio and the gap value to obtain the second actual gear threshold.

[0111] Step S242: Adjust the noise reduction level of the headphone device in the environment according to the first actual level threshold, the second actual level threshold and the A-weighted sound pressure level.

[0112] Figure 6 This is a flowchart illustrating a noise reduction method according to an exemplary embodiment, such as... Figure 6 As shown, step S242 may include the following steps:

[0113] Step S2421: If the A-weighted sound pressure level is greater than the second actual level threshold, switch the noise reduction level from the first level to the second level.

[0114] Step S2422: If the A-weighted sound pressure level is less than the first actual level threshold, switch the noise reduction level from the second level to the first level.

[0115] In one possible implementation, L1 and L2 can represent the first level and the second level, respectively. Figure 7 This is a schematic diagram illustrating the adjustment of noise reduction levels according to an exemplary embodiment, such as... Figure 7 As shown, T1 and T2 represent the first preset gear threshold and the second preset gear threshold, respectively, and T1' and T2' represent the first actual gear threshold and the second actual gear threshold, respectively. The relationship between the first gear level L1 and the second gear level L2 is L1 < L2. For example, when spl(A) > T2', the gear level is switched from L1 to L2; when spl(A) < T1', the gear level is switched from L2 to L1.

[0116] Understandably, the number of noise reduction levels can be set according to the actual application scenario; for example, it can be set from L1 to L. 10 Ten gear levels, correspondingly, can be configured from T1' to T. 10 Adjust the gear according to the actual gear threshold.

[0117] Furthermore, after determining the final gear level, environmental adaptive noise reduction can be achieved by switching gears.

[0118] In the above technical solution, the audio signal to be processed is subjected to a short-time Fourier transform to obtain the target audio signal. Then, the filter coefficients corresponding to the target audio signal are determined. Next, based on the filter coefficients and the target audio signal, the sound pressure level (SPL) and subband energy ratio of the target audio signal are determined. Then, based on the subband energy ratio and the SPL information, the noise reduction level of the headphone device is adjusted according to the environment. Finally, noise reduction is performed on the target audio signal based on the noise reduction level. This technical solution dynamically adjusts the noise reduction level based on the subband energy ratio and the SPL information to achieve noise reduction. This method does not require pre-setting multiple template coefficients and avoids the instability and inaccuracy problems of extracting feature values ​​from environmental acoustic signals. Therefore, it can improve the stability and accuracy of noise reduction while consuming fewer computational resources and reducing hardware costs.

[0119] Figure 8 This is a block diagram illustrating a noise cancellation device 800 according to an exemplary embodiment, the device being applied to an earphone device, see reference. Figure 8 The device 800 includes a processing module 810, a first determining module 820, a second determining module 830, an adjustment module 840, and a noise reduction module 850.

[0120] The processing module 810 is configured to perform a short-time Fourier transform on the audio signal to be processed to obtain the target audio signal.

[0121] The first determining module 820 is configured to determine the filter coefficients corresponding to the target audio signal;

[0122] The second determining module 830 is further configured to determine the sound pressure level information and subband energy ratio of the target audio signal based on the filter coefficients and the target audio signal;

[0123] The adjustment module 840 is configured to adjust the noise reduction level of the headphone device in the environment based on the subband energy ratio and the sound pressure level information.

[0124] The noise reduction module 850 is configured to reduce the noise of the target audio signal according to the noise reduction level.

[0125] Optionally, the filter coefficients are A-weighted filter coefficients, and the second determining module 830 is configured to:

[0126] Based on the A-weighted filter coefficients and the target audio signal, determine the A-weighted sound pressure level of the target audio signal;

[0127] Based on the target audio signal, determine the sub-band energy percentage of the target audio signal.

[0128] Optionally, the second determining module 830 is configured to:

[0129] The first data of the i-th frequency point in the k-th frame of the target audio signal is multiplied by the A-weighted filter coefficient of the i-th frequency point in the k-th frame to obtain the second data of the i-th frequency point in the k-th frame; where k and i are positive integers and their initial values ​​are 1.

[0130] After setting i = i + 1, the step of performing the dot product operation on the first data of the i-th frequency point of the k-th frame of the target audio signal and the A-weighted filter coefficient of the i-th frequency point of the k-th frame to obtain the second data of the i-th frequency point of the k-th frame is executed again until the second data of each frequency point of the k-th frame is obtained.

[0131] After obtaining the second data of each frequency point of each frame in the target audio signal, the A-weighted sound pressure level of the target audio signal is obtained by performing calculations based on the second data of each frequency point of each frame.

[0132] Optionally, the second determining module 830 is configured to:

[0133] The first value is obtained by summing the squares of the first data points of each frequency point in the first sub-band of the k-th frame of the target audio signal.

[0134] The second value is obtained by summing the squares of the first data points of each frequency point of the second sub-band of the k-th frame of the target audio signal.

[0135] The ratio of the first value to the second value is obtained as the sub-band energy ratio of the k-th frame; the k-th frame is any frame in the target audio signal.

[0136] Optionally, the adjustment module 840 includes: a determination submodule and an adjustment submodule;

[0137] The determining submodule is configured to determine the adjusted first actual gear threshold and second actual gear threshold based on the first preset gear threshold, the second preset gear threshold, the energy ratio of the sub-band, and the set gap value.

[0138] The determining submodule is configured to adjust the noise reduction level of the headphone device in the environment based on the first actual level threshold, the second actual level threshold, and the A-weighted sound pressure level.

[0139] Optionally, the determination submodule is configured as follows:

[0140] The first preset threshold value is obtained by subtracting the product of the sub-band energy ratio and the gap value.

[0141] The second preset gear threshold is added to the product of the sub-band energy ratio and the gap value to obtain the second actual gear threshold.

[0142] Optionally, this adjustment submodule is configured as follows:

[0143] If the A-weighted sound pressure level is greater than the second actual level threshold, the noise reduction level will be switched from the first level to the second level.

[0144] If the A-weighted sound pressure level is less than the first actual level threshold, the noise reduction level is switched from the second level to the first level.

[0145] Optionally, the first determining module 820 is configured as follows:

[0146] By combining data on human hearing perception characteristics and the frequency of the target audio signal, the A-weighted filter coefficients are obtained using the A-weighted filter coefficient calculation formula.

[0147] Optionally, the processing module 810 is also configured to:

[0148] Acquire sound signals in the current environment;

[0149] The sound signal is segmented into frames to obtain the audio signal to be processed.

[0150] In the above technical solution, the audio signal to be processed is subjected to a short-time Fourier transform to obtain the target audio signal. Then, the filter coefficients corresponding to the target audio signal are determined. Next, based on the filter coefficients and the target audio signal, the sound pressure level (SPL) and subband energy ratio of the target audio signal are determined. Then, based on the subband energy ratio and the SPL information, the noise reduction level of the headphone device is adjusted according to the environment. Finally, noise reduction is performed on the target audio signal based on the noise reduction level. This technical solution dynamically adjusts the noise reduction level based on the subband energy ratio and the SPL information to achieve noise reduction. This method does not require pre-setting multiple template coefficients and avoids the instability and inaccuracy problems of extracting feature values ​​from environmental acoustic signals. Therefore, it can improve the stability and accuracy of noise reduction while consuming fewer computational resources and reducing hardware costs.

[0151] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0152] This disclosure also provides a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the steps of the noise reduction method provided in this disclosure.

[0153] Figure 9 This is a block diagram illustrating an earphone device 900 according to an exemplary embodiment, with reference to... Figure 9 The headset 900 may include one or more of the following components: processing component 902, memory 904, power supply component 906, multimedia component 908, audio component 910, input / output interface 912, sensor component 914, and communication component 916.

[0154] Processing component 902 typically controls the overall operation of headphone device 900, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 902 may include one or more processors 920 to execute instructions to complete all or part of the steps of the noise reduction method described above. Furthermore, processing component 902 may include one or more modules to facilitate interaction between processing component 902 and other components. For example, processing component 902 may include a multimedia module to facilitate interaction between multimedia component 908 and processing component 902.

[0155] Memory 904 is configured to store various types of data to support the operation of the headset device 900. Examples of this data include instructions for any application or method operating on the headset device 900, contact data, phonebook data, messages, pictures, videos, etc. Memory 904 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0156] Power supply component 906 provides power to various components of headphone device 900. Power supply component 906 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to headphone device 900.

[0157] Multimedia component 908 includes a screen that provides an output interface between the headset device 900 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 908 includes a front-facing camera and / or a rear-facing camera. When the headset device 900 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0158] Audio component 910 is configured to output and / or input audio signals. For example, audio component 910 includes a microphone (MIC) configured to receive external audio signals when the headset device 900 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 904 or transmitted via communication component 916. In some embodiments, audio component 910 also includes a speaker for outputting audio signals.

[0159] Input / output interface 912 provides an interface between processing component 902 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, start buttons, and lock buttons.

[0160] Sensor assembly 914 includes one or more sensors for providing status assessments of various aspects of the headphone device 900. For example, sensor assembly 914 may detect the on / off state of the headphone device 900, the relative positioning of components such as the display and keypad of the headphone device 900, changes in position of the headphone device 900 or one of its components, the presence or absence of user contact with the headphone device 900, the orientation or acceleration / deceleration of the headphone device 900, and temperature changes of the headphone device 900. Sensor assembly 914 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 914 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 914 may also include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.

[0161] Communication component 916 is configured to facilitate wired or wireless communication between headset device 900 and other devices. Headset device 900 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 916 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 916 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0162] In an exemplary embodiment, the headphone device 900 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the noise reduction method described above.

[0163] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 904 including instructions, which can be executed by a processor 920 of the headphone device 900 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0164] The aforementioned device can be a standalone electronic device or a part of a standalone electronic device. For example, in one embodiment, the device can be an integrated circuit (IC) or a chip, wherein the integrated circuit can be a single IC or a collection of multiple ICs. The chip can include, but is not limited to, the following types: GPU (Graphics Processing Unit), CPU (Central Processing Unit), FPGA (Field Programmable Gate Array), DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit), and SoC (System on Chip). The aforementioned integrated circuit or chip can be used to execute executable instructions (or code) to implement the aforementioned noise reduction method. The executable instructions can be stored in the integrated circuit or chip or obtained from other devices or equipment. For example, the integrated circuit or chip includes a processor, memory, and an interface for communicating with other devices. The executable instructions can be stored in the memory, and when the executable instructions are executed by the processor, the above-mentioned noise reduction method is implemented; or, the integrated circuit or chip can receive the executable instructions through the interface and transmit them to the processor for execution to implement the above-mentioned noise reduction method.

[0165] In another exemplary embodiment, a computer program product is also provided, the computer program product comprising a computer program executable by a programmable device, the computer program having a code portion for performing the noise reduction method described above when executed by the programmable device.

[0166] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of this disclosure. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0167] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A noise reduction method, characterized in that, Applied to headphone devices, the method includes: The target audio signal is obtained by performing a short-time Fourier transform on the audio signal to be processed. Determine the filter coefficients corresponding to the target audio signal; Based on the filter coefficients and the target audio signal, the sound pressure level information and sub-band energy ratio of the target audio signal are determined, wherein the sound pressure level information is the A-weighted sound pressure level; Based on the subband energy ratio and the sound pressure level information, adjust the noise cancellation level of the headphone device in the corresponding environment; The target audio signal is noise-reduced according to the noise reduction level; The step of adjusting the noise cancellation level of the headphone device in the given environment based on the sub-band energy ratio and the sound pressure level information includes: The first preset gear threshold is obtained by subtracting the product of the sub-band energy ratio and the set gap value. The second preset threshold value is added to the product of the sub-band energy ratio and the gap value to obtain the second actual threshold value; Based on the first actual level threshold, the second actual level threshold, and the A-weighted sound pressure level, the noise cancellation level of the headphone device is adjusted to the corresponding level in the environment. The sub-band energy percentage of the target audio signal is determined in the following way: The first value is obtained by summing the squares of the first data points of each frequency point of the first sub-band of the k-th frame of the target audio signal. The second value is obtained by summing the squares of the first data points of each frequency point of the second sub-band of the k-th frame of the target audio signal; The ratio of the first value to the second value is obtained as the sub-band energy ratio of the k-th frame; the k-th frame is any frame in the target audio signal.

2. The method according to claim 1, characterized in that, The filter coefficients are A-weighted filter coefficients. The step of determining the sound pressure level information and sub-band energy proportion of the target audio signal based on the filter coefficients and the target audio signal includes: The A-weighted sound pressure level of the target audio signal is determined based on the A-weighted filter coefficients and the target audio signal. Based on the target audio signal, determine the sub-band energy percentage of the target audio signal.

3. The method according to claim 2, characterized in that, The step of determining the A-weighted sound pressure level of the target audio signal based on the A-weighted filter coefficients and the target audio signal includes: The first data of the i-th frequency point in the k-th frame of the target audio signal is multiplied by the A-weighted filter coefficients of the i-th frequency point in the k-th frame to obtain the second data of the i-th frequency point in the k-th frame; where k and i are positive integers and their initial values ​​are 1. After setting i=i+1, the step of performing the dot product operation on the first data of the i-th frequency point of the k-th frame in the target audio signal and the A-weighted filter coefficient of the i-th frequency point of the k-th frame to obtain the second data of the i-th frequency point of the k-th frame is executed again until the second data of each frequency point of the k-th frame is obtained. After obtaining the second data of each frequency point of each frame in the target audio signal, the A-weighted sound pressure level of the target audio signal is obtained by performing calculations based on the second data of each frequency point of each frame.

4. The method according to claim 1, characterized in that, The step of adjusting the noise cancellation level of the headphone device in the given environment based on the first actual noise cancellation threshold, the second actual noise cancellation threshold, and the A-weighted sound pressure level includes: If the A-weighted sound pressure level is greater than the second actual gear threshold, the noise reduction gear level will be switched from the first level to the second level. If the A-weighted sound pressure level is less than the first actual gear threshold, the noise reduction gear level is switched from the second level to the first level.

5. The method according to claim 1, characterized in that, The filter coefficients are A-weighted filter coefficients, and determining the filter coefficients corresponding to the target audio signal includes: Based on data on human auditory perception characteristics and the frequency of the target audio signal, the A-weighted filter coefficients are obtained using the A-weighted filter coefficient calculation formula.

6. The method according to claim 1, characterized in that, The method further includes: Acquire sound signals in the current environment; The sound signal is processed by frame segmentation to obtain the audio signal to be processed.

7. A noise reduction device, characterized in that, Applied to headphone devices, the device includes: The processing module is configured to perform a short-time Fourier transform on the audio signal to be processed to obtain the target audio signal. The first determining module is configured to determine the filter coefficients corresponding to the target audio signal; The second determining module is configured to determine the sound pressure level information and sub-band energy ratio of the target audio signal based on the filter coefficients and the target audio signal, wherein the sound pressure level information is an A-weighted sound pressure level; The adjustment module is configured to adjust the noise cancellation level of the headphone device in the given environment based on the subband energy ratio and the sound pressure level information. The noise reduction module is configured to reduce the noise of the target audio signal according to the noise reduction level. The adjustment module includes: a determination submodule and an adjustment submodule; The determining submodule is configured to subtract the product of the first preset gear threshold and the sub-band energy ratio and the set gap value to obtain the first actual gear threshold; and to add the second preset gear threshold to the product of the sub-band energy ratio and the gap value to obtain the second actual gear threshold. The adjustment submodule is configured to adjust the noise reduction level of the headphone device in the environment based on the first actual level threshold, the second actual level threshold, and the A-weighted sound pressure level. The second determining module is configured to obtain the sum of squares of the first data of each frequency point of the first sub-band of the k-th frame of the target audio signal to obtain a first value; obtain the sum of squares of the first data of each frequency point of the second sub-band of the k-th frame of the target audio signal to obtain a second value; obtain the ratio of the first value to the second value as the sub-band energy proportion of the k-th frame; the k-th frame is any frame in the target audio signal.

8. A headphone device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to execute the executable instructions to implement the noise reduction method according to any one of claims 1-6.

9. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When executed by a processor, the program instructions implement the steps of the method described in any one of claims 1-6.

10. A chip, characterized in that, It includes a processor and an interface; the processor is used to read instructions to perform the steps of the method according to any one of claims 1-6.

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