Sound quality adjustment method, device, equipment and storage medium

Abstract

The present invention relates to the field of audio transmission technologies, and particularly to a sound quality adjustment method, apparatus, device, and storage medium. The present invention processes an initial sound signal to obtain target sub-band parameters of a target frequency band signal, and calculates parameters of the target frequency band signal through a preset frequency band parameter calculation model to obtain accurate target sub-band energy parameters. The target sub-band signal is filtered through a preset filtering model using the target sub-band parameters and the target sub-band energy parameters, effectively filtering each sub-band of the initial sound signal, reducing the feedback phenomenon during the amplification of the initial sound signal, avoiding the problem that when a microphone picks up and amplifies sound, there will be amplification feedback, resulting in a decrease in audio clarity and seriously affecting the user's listening experience, and enhancing the sound quality effect of the microphone during amplification.

Description

Technical Field

[0001] The present invention relates to the technical field of audio transmission, and particularly to a method, device, equipment and storage medium for sound quality adjustment. Background Art

[0002] When amplifying audio in a traditional classroom, a head-mounted wired microphone is generally used for sound pickup, and then amplified by a loudspeaker. However, there is a drawback that it needs to be worn on the head for a long time, and a transmitting device also needs to be hung on the waist. It needs to be charged regularly, resulting in poor user experience. With the development of technology, omnidirectional microphones, directional microphones or array microphones are now introduced for local sound amplification. The above microphones are all highly sensitive and have a long sound pickup distance, so they are very likely to generate feedback, resulting in phenomena such as sound tailing and metallic sound in the amplified sound quality, and heavy reverberation, leading to a decrease in clarity and seriously affecting the user's listening experience.

[0003] The above content is only used to assist in understanding the technical solution of the present invention, and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main object of the present invention is to provide a sound quality adjustment, aiming to solve the technical problem that when a microphone is used for sound pickup and amplification in the prior art, there will be amplification feedback, resulting in a decrease in audio clarity and seriously affecting the user's listening experience.

[0005] To achieve the above object, the present invention provides a method for sound quality adjustment, the method comprising the following steps:

[0006] Obtain an initial sound signal;

[0007] Determine a target frequency band signal according to the initial sound signal, and extract target sub-band parameters of the target frequency band signal;

[0008] Calculate parameters according to the target frequency band signal through a preset frequency band parameter calculation model to obtain target sub-band energy parameters;

[0009] Perform signal filtering on the target frequency band signal based on the target sub-band parameters and the target sub-band energy parameters through a preset filtering model to obtain a target sound signal.

[0010] Optionally, the performing signal filtering on the target frequency band signal based on the target sub-band parameters and the target sub-band energy parameters through a preset filtering model to obtain a target sound signal includes:

[0011] Obtain an expected signal, and obtain an expected signal corresponding to the expected signal;

[0012] Update the filter model coefficients in the preset filter model according to the target sub-band parameters, the target sub-band energy parameters, and the desired signal, to obtain a target filter model;

[0013] Filter the target band signal through the target filter model to obtain a target sound signal.

[0014] Optionally, the step of updating the filter model coefficients in the preset filter model according to the target sub-band parameters, the target sub-band energy parameters, and the desired signal, to obtain a target filter model, includes:

[0015] Obtain the current environmental information, and determine the corresponding sound attenuation coefficient based on the current environmental information;

[0016] Determine a convergence factor parameter according to the sound attenuation coefficient, the target sub-band energy parameters, and the target sub-band parameters;

[0017] Determine a tap weight vector based on the convergence factor parameter;

[0018] Update the filter model coefficients in the preset filter model according to the convergence factor parameter, the desired signal, and the tap weight vector, to obtain a target filter model.

[0019] Optionally, the step of determining a convergence factor parameter according to the sound attenuation coefficient, the target sub-band energy parameters, and the target sub-band parameters, includes:

[0020] Extract the sub-band number value in the target sub-band parameters;

[0021] Determine a sound attenuation weight according to the sound attenuation coefficient;

[0022] Obtain a target band energy weight based on the target sub-band energy parameters and the sub-band number value;

[0023] Determine a convergence factor parameter based on the sound attenuation weight, the target band energy weight, and the target sub-band parameters.

[0024] Optionally, the step of determining a convergence factor parameter based on the sound attenuation weight, the target band energy weight, and the target sub-band parameters, includes:

[0025] Determine a target band adaptive factor based on the sound attenuation weight and the target band energy weight;

[0026] Perform data processing according to the target band adaptive factor and the target sub-band parameters to obtain a convergence factor parameter.

[0027] Optionally, the step of calculating the target sub-band energy parameters by calculating parameters through a preset band parameter calculation model according to the target band signal, includes:

[0028] Perform signal filtering on the target band signal to obtain a filtered signal;

[0029] Perform parameter calculation on the filtered signal through a preset band parameter calculation model to obtain target sub-band energy parameters, where the target sub-band energy parameters include: a target band energy value and a target band energy weight coefficient.

[0030] Optionally, the performing parameter calculation on the filtered signal through a preset band parameter calculation model to obtain target sub-band energy parameters includes:

[0031] Extract the frame length data of the filtered signal;

[0032] Perform parameter calculation on the filtered signal through a preset band parameter calculation model based on the frame length data to obtain a target floating-point value corresponding to the filtered signal;

[0033] Determine target sub-band energy parameters according to the target floating-point value and the frame length data, and perform data processing according to the target sub-band energy parameters to obtain a target band energy weight coefficient.

[0034] In addition, to achieve the above object, the present invention also proposes a sound quality adjustment device, where the sound quality adjustment device includes:

[0035] A signal acquisition module, configured to acquire an initial sound signal;

[0036] A parameter acquisition module, configured to determine a target band signal according to the initial sound signal and extract target sub-band parameters of the target band signal;

[0037] A parameter calculation module, configured to perform parameter calculation on the target band signal through a preset band parameter calculation model to obtain target sub-band energy parameters;

[0038] A signal filtering module, configured to perform signal filtering on the target band signal through a preset filtering model based on the target sub-band parameters and the target sub-band energy parameters to obtain a target sound signal.

[0039] In addition, to achieve the above object, the present invention also proposes a sound quality adjustment device, where the sound quality adjustment device includes: a memory, a processor, and a sound quality adjustment program stored on the memory and executable on the processor, and the sound quality adjustment program is configured to implement the steps of the sound quality adjustment method as described above.

[0040] In addition, to achieve the above object, the present invention also proposes a storage medium, where a sound quality adjustment program is stored on the storage medium, and when the sound quality adjustment program is executed by a processor, the steps of the sound quality adjustment method as described above are implemented.

[0041] The present invention obtains an initial sound signal, determines a target frequency band signal according to the initial sound signal, extracts target sub-band parameters of the target frequency band signal, performs parameter calculation on the target frequency band signal through a preset frequency band parameter calculation model to obtain target sub-band energy parameters, and filters the target frequency band signal through a preset filtering model based on the target sub-band parameters and the target sub-band energy parameters to obtain a target sound signal. Compared with the prior art, the present invention processes the initial sound signal, obtains the target sub-band parameters of the target frequency band signal, performs parameter calculation on the target frequency band signal through a preset frequency band parameter calculation model to obtain accurate target sub-band energy parameters, and filters the target frequency band signal through a preset filtering model based on the target sub-band parameters and the target sub-band energy parameters, effectively filtering each sub-band of the initial sound signal, so as to reduce the feedback phenomenon during the amplification of the initial sound signal, avoid the problem that when a microphone picks up and amplifies sound, there will be amplification feedback, resulting in a decrease in audio clarity and seriously affecting the user's listening experience, and enhancing the sound quality effect of the microphone during amplification. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is a schematic structural diagram of a sound quality adjustment device in a hardware operating environment related to the solution of an embodiment of the present invention;

[0043] Figure 2 is a schematic flowchart of the first embodiment of the sound quality adjustment method of the present invention;

[0044] Figure 3 is a schematic flowchart of the second embodiment of the sound quality adjustment method of the present invention;

[0045] Figure 4 is a structural block diagram of the first embodiment of the sound quality adjustment device of the present invention.

[0046] The implementation, functional features, and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0048] Refer to Figure 1 , Figure 1 is a schematic structural diagram of a sound quality adjustment device in a hardware operating environment related to the solution of an embodiment of the present invention.

[0049] As Figure 1As shown in the figure, the sound quality adjustment device may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to realize the connection and communication between these components. The user interface 1003 may include a display screen and an input unit such as a keyboard. Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a Wireless-Fidelity (Wi-Fi) interface). The memory 1005 may be a high-speed Random Access Memory (RAM) or a stable Non-Volatile Memory (NVM), such as a disk memory. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0050] Those skilled in the art can understand that Figure 1 the structure shown in the figure does not constitute a limitation on the sound quality adjustment device, and it may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0051] As Figure 1 shown, the memory 1005, as a storage medium, may include an operating system, a network communication module, a user interface module, and a sound quality adjustment program.

[0052] In Figure 1 the sound quality adjustment device shown in the figure, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the sound quality adjustment device of the present invention may be provided in the sound quality adjustment device. The sound quality adjustment device calls the sound quality adjustment program stored in the memory 1005 through the processor 1001 and executes the sound quality adjustment method provided by the embodiments of the present invention.

[0053] The embodiments of the present invention provide a sound quality adjustment method. Referring to Figure 2 , Figure 2 it is a schematic flowchart of the first embodiment of a sound quality adjustment method of the present invention.

[0054] In this embodiment, the sound quality adjustment method includes the following steps:

[0055] Step S10: Obtain an initial sound signal.

[0056] It should be noted that the execution subject of this embodiment is a sound quality adjustment device. Among them, the sound quality adjustment device can be a device with data processing and data transmission capabilities, or it can be an electronic device such as a control computer, a mobile phone, or a tablet computer. This embodiment does not make specific restrictions in this regard. In this embodiment and the following embodiments, a control computer will be used as an example for illustration.

[0057] It can be understood that the initial sound signal can be a sound signal collected by a sound collection device. Among them, the sound collection device can be a microphone or a sound collection card, etc. This embodiment does not make specific restrictions in this regard. In this embodiment, a high-sensitivity microphone will be used as an example for illustration.

[0058] In addition, the signal transmission method of the initial sound signal, that is, the transmission of the initial sound signal can be carried out through a wired cable, or it can be carried out through a wireless communication method such as wireless Bluetooth. This embodiment does not make specific restrictions in this regard.

[0059] Step S20: Determine the target band signal according to the initial sound signal, and extract the target sub-band parameters of the target band signal.

[0060] It should be noted that the target band signal is used to calculate the energy parameter of the target band, so that the target high-frequency sound signal is at an appropriate convergence speed. For example: Since the high-frequency signal in the sound signal significantly attenuates faster during propagation and the low-frequency signal attenuates slower, after adjustment, the attenuation speed of the high-frequency signal can be made slower and the attenuation speed of the low-frequency signal can be made faster.

[0061] In addition, the target band signal can be obtained by dividing the initial sound signal according to the frequency band bandwidth to obtain band sound signals with different center frequencies. Referring to Table 1, the initial sound signal can be divided according to the center frequency and frequency band bandwidth in Table 1 according to the frequency band bandwidth.

[0062]

[0063] Table 1: Octave parameter table

[0064] It is worth noting that the target sub-band parameters can be the frequency band signal bandwidth, frequency band signal resolution, sampling rate, etc. of the target band signal, or the number of sub-bands after splitting the initial sound signal, etc. This embodiment does not make specific restrictions in this regard.

[0065] Step S30: Perform parameter calculation according to the target band signal through a preset frequency band parameter calculation model to obtain the target sub-band energy parameter.

[0066] It can be understood that the preset frequency band parameter calculation model is used to calculate the band energy of the target frequency band signal and the weight coefficient of the band energy in the weighted band energy, and record the band energy of the target frequency band signal and the weight coefficient of the band energy in the weighted band energy as the target sub-band energy parameter for storage.

[0067] It should be noted that when calculating the target sub-band energy parameter, the initial sound signal can be filtered through a filter. In addition, when the initial sound signal is divided into frequency bands, since the center frequencies of the divided target frequency band signals are different, when setting the parameters of the filter, the filter parameters need to be set according to the center frequencies of different target frequency band signals. In this embodiment, the filter for filtering the initial sound signal can be a combination of a low-pass filter and a high-pass filter to obtain a band-pass filter and improve the filtering efficiency of the filter.

[0068] Step S40: Based on the target sub-band parameter and the target sub-band energy parameter, perform signal filtering on the target frequency band signal through a preset filtering model to obtain a target sound signal.

[0069] It should be noted that the preset filtering model is used to adjust the convergence factor of the target frequency band signal based on the target sub-band parameter and the target sub-band energy parameter, and perform a linear connection change on the target frequency band signal after adjusting the convergence factor to obtain a target sound signal, where the target sound signal can be a sound signal obtained by linearly connecting a high-frequency signal with a reduced attenuation speed or a low-frequency signal with an increased attenuation speed. This embodiment does not make specific limitations on this.

[0070] In a specific implementation, by adaptively filtering the band energy parameter and the band parameter of different frequency band signals to adjust the attenuation speed of the target frequency band adaptive factor corresponding to different frequency bands, the high-frequency signal and the low-frequency signal of the output sound signal can exist simultaneously, so as to reduce the feedback phenomenon in the actual application process.

[0071] In this embodiment, an initial sound signal is obtained, a target band signal is determined according to the initial sound signal, target sub-band parameters of the target band signal are extracted, parameter calculation is performed on the target band signal through a preset band parameter calculation model to obtain target sub-band energy parameters, and based on the target sub-band parameters and the target sub-band energy parameters, the target band signal is signal-filtered through a preset filtering model to obtain a target sound signal. In this embodiment, by processing the initial sound signal, target sub-band parameters of the target band signal are obtained, and the target band signal is parameter-calculated through a preset band parameter calculation model to obtain accurate target sub-band energy parameters. Through the target sub-band parameters and the target sub-band energy parameters, the target band signal is signal-filtered through a preset filtering model, effectively signal-filtering each sub-band of the initial sound signal to reduce the feedback phenomenon during the amplification of the initial sound signal, avoiding the technical problem that when the microphone picks up and amplifies sound, there will be amplification feedback, resulting in a decrease in audio clarity and seriously affecting the user's listening experience, and enhancing the sound quality effect of the microphone during amplification.

[0072] Reference Figure 3 , Figure 3 is a schematic flowchart of the second embodiment of a sound quality adjustment method of the present invention.

[0073] Based on the above first embodiment, in this embodiment, step S30 includes:

[0074] Step S301: Signal-filter the target band signal to obtain a filtered signal.

[0075] It should be noted that in this embodiment, a filter is preset to signal-filter the target band signal. Among them, the preset filter uses a low-pass filter and a high-pass filter to form a band-pass filter. For example, in actual operation, a 10HZ high-pass filter and a 10000HZ low-pass filter are used to form a band-pass filter to screen out sound signals with a frequency range between 10HZ and 10000HZ.

[0076] Step S302: Perform parameter calculation on the filtered signal through a preset band parameter calculation model to obtain target sub-band energy parameters, where the target sub-band energy parameters include: a target band energy value and a target band energy weight coefficient.

[0077] It can be understood that parameter calculation is performed on the filtered signal to obtain the band energy and the weight coefficient of the band energy in the weighted band energy, and the band energy and the weight coefficient of the band energy in the weighted band energy of the target band signal are recorded as target sub-band energy parameters for storage.

[0078] Further, in order to accurately obtain the band energy and the band energy weight coefficient, step S302 includes:

[0079] Extract the frame length data of the filtered signal;

[0080] Based on the frame length data, perform parameter calculation on the filtered signal through a preset band parameter calculation model to obtain the target floating-point value corresponding to the filtered signal. The target floating-point value can be a normalized floating-point value.

[0081] Determine the target sub-band energy parameter according to the target floating-point value and the frame length data, and perform data processing according to the target sub-band energy parameter to obtain the target band energy weight coefficient.

[0082] It should be noted that the frame length data can be the number of sampling points within a preset time period. In actual operation, taking a sampling rate of 48000 Hz and 256 points as the frame length as an example, there are 187 frames in 1 second.

[0083] In the actual process, in order to reduce the influence of data errors, a biquadratic filter needs to be used to calculate the normalized floating-point value after signal filtering to reduce the error in calculating the band energy. The specific formula for obtaining the normalized floating-point value after signal filtering is:

[0084]

[0085] Among them, a0, a1, a2 and b0, b1, b2 are the parameter data of the high-pass and low-pass filters, and they have different values in the high-pass and low-pass filters.

[0086] It is easy to understand that after obtaining the normalized floating-point value after signal filtering, the specific formula for obtaining the band energy according to the obtained frame length data and the normalized floating-point value after signal filtering is:

[0087]

[0088] Among them, len is the frame length data, and x i is the normalized floating-point value of the filtered signal after sampling.

[0089] In addition, the specific formula for obtaining the band energy weight coefficient based on the band energy is:

[0090]

[0091] Among them, Lrms i is the energy value of the i-th octave band, and N is the number of bands.

[0092] In this embodiment, step S40 includes:

[0093] Step S401: Obtain the desired signal.

[0094] It should be noted that the desired signal can be a signal obtained by delaying the initial sound signal by a certain amount. The delayed input sound signal is used as the desired response signal to update the filter coefficients through the filter coefficient update equation.

[0095] In addition, in order to better filter the signal, it is also necessary to update the filter coefficients according to different frequency band signals to distinguish different impulse responses acting on the filter. When updating the filter coefficients, the normalized least mean square (NLMS) method can be used to calculate the updated filter coefficients. In actual operation, for an NLMS filter with a length of N, the iterative update formula for the filter coefficients is:

[0096]

[0097] where e is the error signal, d is the desired signal, and y is the estimated signal.

[0098] It is easy to understand that the error signal is the difference between the desired signal and the estimated signal. The estimated signal can be the output signal of the filter. The error signal is obtained based on the desired signal and the estimated signal of the filter to update the filter coefficients.

[0099] Step S402: Update the filter model coefficients in the preset filter model according to the target sub-band parameters, the target sub-band energy parameters, and the desired signal to obtain the target filter model.

[0100] It should be noted that the filter model coefficients in the preset filter model can be updated through the target sub-band parameters, the target sub-band energy parameters, and the desired signal. The specific formula is:

[0101]

[0102] where u is the convergence factor, and the value range is 0 < u < 1. w is the filter tap weight vector. The coefficient that needs to be dynamically adjusted in this embodiment is u.

[0103] Furthermore, in order to obtain the tap weight vector and the convergence factor, step S402 includes:

[0104] Obtain the current environmental information and determine the corresponding sound attenuation coefficient based on the current environmental information;

[0105] Determine the convergence factor parameter according to the sound attenuation coefficient, the target sub-band energy parameters, and the target sub-band parameters;

[0106] Determine the tap weight vector based on the convergence factor parameter;

[0107] Update the filter model coefficients in the preset filter model according to the convergence factor parameter, the desired signal and the tap weight vector to obtain the target filter model.

[0108] It should be noted that the sound attenuation coefficient can be obtained by taking the average value of the atmospheric attenuation coefficient with noise in the spare parts in combination with various environmental conditions. Among them, the atmospheric attenuation coefficient can be obtained by querying in the preset atmospheric attenuation coefficient table for the current environment. Referring to Table 2, the preset atmospheric sound attenuation table can be obtained.

[0109]

[0110] Table 2: Atmospheric sound attenuation coefficient table

[0111] In specific implementation, query the average value of the atmospheric sound attenuation coefficient of the corresponding frequency band in the preset atmospheric sound attenuation coefficient table according to the current environmental information. For example: 63Hz - 0.13, 125Hz - 0.4, 250Hz - 1.1, 500Hz - 2.5, 1kHz - 5.4, 2kHz - 13.1, 3kHz - 37.9, 8kHz - 111.

[0112] Furthermore, in order to obtain the convergence factor, it is also necessary to extract the number of sub - frequency bands value in the target sub - band parameters, determine the sound attenuation weight according to the sound attenuation coefficient, obtain the target frequency band energy weight based on the target sub - band energy parameter and the number of sub - frequency bands value, and determine the convergence factor parameter based on the sound attenuation weight, the target frequency band energy weight and the target sub - band parameters.

[0113] The specific formula for obtaining the sound attenuation weight is:

[0114] ρ i = log 10 (98.2 * α i )

[0115] where ρ is the sound attenuation weight, α is the average value of the atmospheric attenuation coefficient, and i is the frequency band information corresponding to the target frequency band.

[0116] In specific implementation, in this embodiment, when the sound attenuation coefficient of the central frequency 63Hz is p1 = 1.106 and ps = 4.037, it can be seen that the sound attenuation coefficient of the 8kHz high - frequency band is about four times that of the 63Hz low - frequency band.

[0117] The specific formula for obtaining the octave band energy weight coefficient factor is:

[0118] φ i = 1 + 0.3 * (λ i*N-1)

[0119] where φ is the frequency band energy weight;

[0120] Further, after obtaining the sound attenuation weight and the target frequency band energy weight, the target frequency band adaptive factor can be obtained through the sound attenuation weight and the target frequency band energy weight to determine the convergence factor. Therefore, the steps of determining the convergence factor parameter based on the sound attenuation weight, the target frequency band energy weight, and the target sub-band parameter include: determining the target frequency band adaptive factor based on the sound attenuation weight and the target frequency band energy weight, and performing data processing on the target frequency band adaptive factor and the target sub-band parameter to obtain the convergence factor parameter.

[0121] The specific formula for determining the target frequency band adaptive factor according to the sound attenuation weight and the target frequency band energy weight is:

[0122] γ i =0.001*φ i *ρ i

[0123] where φ is the frequency band energy weight and ρ is the sound attenuation weight.

[0124] Step S403: Filter the target frequency band signal through the target filtering model to obtain a target sound signal.

[0125] It should be understood that when filtering the target frequency band signal through the target filtering model, fast Fourier transform (FFT) filtering can be used to filter each frequency band signal, or other modules with filtering functions can be used for filtering processing. This embodiment does not make specific limitations on this.

[0126] Further, after obtaining the sound attenuation weight and the target frequency band energy weight, the target frequency band adaptive factor can be obtained through the sound attenuation weight and the target frequency band energy weight to determine the convergence factor. Therefore, the steps of determining the convergence factor parameter based on the sound attenuation weight, the target frequency band energy weight, and the target sub-band parameter include: determining the target frequency band adaptive factor based on the sound attenuation weight and the target frequency band energy weight, and performing data processing on the target frequency band adaptive factor and the target sub-band parameter to obtain the convergence factor parameter.

[0127] In specific implementation, after completing the sub-band filtering, the 8 sub-band weight coefficients can also be extended to the full-band coefficients to obtain the accurate coefficients of the convergence factor. The specific formula for obtaining the convergence factor is:

[0128]

[0129]

[0130] Among them, Fs is the sampling rate, FL is the FFT length, and Ff is the resolution of the FFT. Bi is the width of each frequency band, Fj corresponds to the relative index of the frequency band region to which it belongs, and u is the convergence factor of the frequency point. Floor is the floor operation.

[0131] In a specific implementation, after obtaining the convergence factor, the convergence factor is substituted into the NLMS formula to obtain an updated tap weight vector, so that the filter performs signal filtering and outputs a target sound signal.

[0132] In this embodiment, by processing the initial sound signal, the target sub-band parameters of the target frequency band signal are obtained, and after the target frequency band signal is filtered by the filter, the filtered signal is calculated by a preset frequency band parameter calculation model to remove the noise signal, so as to obtain the accurate target sub-band energy parameters corresponding to the target frequency band signal, and the desired signal in the target sub-band energy parameters is extracted. The filter model coefficients in the preset filter model are updated by the target sub-band parameters and the target sub-band energy parameters to obtain a target filter model, which can update the coefficients of the filter model in real time to achieve accurate signal filtering. The target frequency band signal is filtered by the target filter model, effectively filtering each sub-band of the initial sound signal, so as to reduce the feedback phenomenon when the initial sound signal is amplified, and avoid the problem that when the microphone picks up and amplifies the sound, there will be amplification feedback, resulting in a decrease in audio clarity and seriously affecting the user's listening experience, and enhancing the sound quality effect of the microphone when amplifying.

[0133] In addition, an embodiment of the present invention also proposes a storage medium, on which a sound quality adjustment program is stored. When the sound quality adjustment program is executed by a processor, the steps of the sound quality adjustment method as described above are implemented.

[0134] Since this storage medium adopts all the technical solutions of the above-mentioned all embodiments, it at least has all the beneficial effects brought by the technical solutions of the above-mentioned embodiments, and will not be elaborated here one by one.

[0135] Refer to Figure 4 , Figure 4 which is the structural block diagram of the first embodiment of the sound quality adjustment device of the present invention.

[0136] As Figure 4 shown, the sound quality adjustment device proposed by the embodiment of the present invention includes:

[0137] A signal acquisition module 10, configured to acquire an initial sound signal.

[0138] The parameter acquisition module 20 is configured to determine a target band signal according to the initial sound signal and extract target sub-band parameters of the target band signal.

[0139] The parameter calculation module 30 is configured to perform parameter calculation on the target band signal through a preset band parameter calculation model to obtain target sub-band energy parameters.

[0140] The signal filtering module 40 is configured to perform signal filtering on the target band signal through a preset filtering model based on the target sub-band parameters and the target sub-band energy parameters to obtain a target sound signal.

[0141] In this embodiment, by processing the initial sound signal, target sub-band parameters of the target band signal are obtained, and the target band signal is subjected to parameter calculation through a preset band parameter calculation model to obtain accurate target sub-band energy parameters. The target band signal is subjected to signal filtering through a preset filtering model based on the target sub-band parameters and the target sub-band energy parameters, effectively performing signal filtering on each sub-band of the initial sound signal, so as to reduce the feedback phenomenon during the amplification of the initial sound signal, and avoid the technical problem that when the microphone picks up and amplifies sound, there will be amplification feedback, resulting in a decrease in audio clarity and seriously affecting the user's listening experience, enhancing the sound quality effect of the microphone during amplification.

[0142] In one embodiment, the signal filtering module 40 is further configured to extract an expected signal from the target band signal and obtain an expected signal corresponding to the expected signal; update the filtering model coefficients in the preset filtering model according to the target sub-band parameters, the target sub-band energy parameters, and the expected signal to obtain a target filtering model; perform signal filtering on the target band signal through the target filtering model to obtain a target sound signal.

[0143] In one embodiment, the signal filtering module 40 is further configured to obtain current environmental information and determine a corresponding sound attenuation coefficient based on the current environmental information; determine a convergence factor parameter according to the sound attenuation coefficient, the target sub-band energy parameters, and the target sub-band parameters; determine a tap weight vector based on the convergence factor parameter; update the filtering model coefficients in the preset filtering model according to the convergence factor parameter, the expected signal, and the tap weight vector to obtain a target filtering model.

[0144] In one embodiment, the signal filtering module 40 is further configured to extract the sub-band number value in the target sub-band parameters; determine a sound attenuation weight according to the sound attenuation coefficient; obtain a target band energy weight based on the target sub-band energy parameters and the sub-band number value; determine a convergence factor parameter based on the sound attenuation weight, the target band energy weight, and the target sub-band parameters.

[0145] In one embodiment, the signal filtering module 40 is further configured to determine a target frequency band adaptive factor based on the acoustic attenuation weight and the target frequency band energy weight; perform data processing according to the target frequency band adaptive factor and the target sub-band parameter to obtain a convergence factor parameter.

[0146] In one embodiment, the parameter calculation module 30 is further configured to perform signal filtering on the target frequency band signal to obtain a filtered signal; perform parameter calculation on the filtered signal through a preset frequency band parameter calculation model to obtain target sub-band energy parameters, where the target sub-band energy parameters include: a target frequency band energy value and a target frequency band energy weight coefficient.

[0147] In one embodiment, the parameter calculation module 30 is further configured to extract the frame length data of the filtered signal; perform parameter calculation on the filtered signal through a preset frequency band parameter calculation model based on the frame length data to obtain a target floating-point value corresponding to the filtered signal; determine the target sub-band energy parameter according to the target floating-point value and the frame length data, and perform data processing according to the target sub-band energy parameter to obtain a target frequency band energy weight coefficient.

[0148] It should be understood that the above is only an example for illustration and does not constitute any limitation to the technical solution of the present invention. In specific applications, those skilled in the art can set according to needs, and the present invention does not limit this.

[0149] It should be noted that the above-described work process is only illustrative and does not limit the protection scope of the present invention. In actual applications, those skilled in the art can select some or all of them according to actual needs to achieve the purpose of the embodiment solution, and there is no limitation here.

[0150] In addition, for the technical details not described in detail in this embodiment, reference can be made to the sound quality adjustment method provided in any embodiment of the present invention, which will not be elaborated here.

[0151] In addition, it should be noted that in this article, the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, article or system. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, method, article or system including that element.

[0152] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0153] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described example methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as a read-only memory (ROM) / RAM, magnetic disk, optical disk), and includes several instructions for causing a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention.

[0154] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall equally be included in the patent protection scope of the present invention.

Claims

1. A method for sound quality adjustment, characterized in that, The sound quality adjustment method includes: Obtain an initial sound signal; Determine a target frequency band signal according to the initial sound signal, and extract target sub-band parameters of the target frequency band signal; Perform parameter calculation on the target frequency band signal through a preset frequency band parameter calculation model to obtain target sub-band energy parameters; Perform signal filtering on the target frequency band signal based on the target sub-band parameters and the target sub-band energy parameters through a preset filtering model to obtain a target sound signal; The performing signal filtering on the target frequency band signal based on the target sub-band parameters and the target sub-band energy parameters through a preset filtering model to obtain a target sound signal includes: Obtain an expected signal; Update the filtering model coefficients in the preset filtering model according to the target sub-band parameters, the target sub-band energy parameters, and the expected signal to obtain a target filtering model; Perform signal filtering on the target frequency band signal through the target filtering model to obtain a target sound signal; The updating the filtering model coefficients in the preset filtering model according to the target sub-band parameters, the target sub-band energy parameters, and the expected signal to obtain a target filtering model includes: Obtain current environmental information, and determine a corresponding sound attenuation coefficient based on the current environmental information; Determine a convergence factor parameter according to the sound attenuation coefficient, the target sub-band energy parameters, and the target sub-band parameters; Determine a tap weight vector based on the convergence factor parameter; Update the filtering model coefficients in the preset filtering model according to the convergence factor parameter, the expected signal, and the tap weight vector to obtain a target filtering model.

2. The sound quality adjustment method according to claim 1, wherein The determining a convergence factor parameter according to the sound attenuation coefficient and the target sub-band energy parameters includes: Extract the sub-band number value in the target sub-band parameters; Determine a sound attenuation weight according to the sound attenuation coefficient; Obtain a target frequency band energy weight based on the target sub-band energy parameters and the sub-band number value; Determine a convergence factor parameter based on the sound attenuation weight and the target frequency band energy weight.

3. The sound quality adjustment method according to claim 2, wherein, The determining a convergence factor parameter based on the sound attenuation weight and the target frequency band energy weight includes: Determine a target frequency band adaptation factor based on the sound attenuation weight and the target frequency band energy weight; Perform data processing on the target frequency band adaptation factor and the target sub-band parameters to obtain a convergence factor parameter.

4. The sound quality adjustment method according to any one of claims 1 to 3, characterized in that The performing parameter calculation on the target frequency band signal through a preset frequency band parameter calculation model to obtain target sub-band energy parameters includes: Perform signal filtering on the target frequency band signal to obtain a filtered signal; Perform parameter calculation on the filtered signal through a preset frequency band parameter calculation model to obtain target sub-band energy parameters, where the target sub-band energy parameters include: a target frequency band energy value and a target frequency band energy weight coefficient.

5. The sound quality adjustment method according to claim 4, wherein The performing parameter calculation on the filtered signal through a preset frequency band parameter calculation model to obtain target sub-band energy parameters includes: Extract the frame length data of the filtered signal; Perform parameter calculation on the filtered signal through a preset frequency band parameter calculation model based on the frame length data to obtain a target floating point value corresponding to the filtered signal; Determine the target sub-band energy parameter according to the target floating-point value and the frame length data, and perform data processing according to the target sub-band energy parameter to obtain the target frequency band energy weight coefficient.

6. An audio quality adjustment device, characterized in that, The sound quality adjustment device includes: A signal acquisition module, configured to acquire an initial sound signal; A parameter acquisition module, configured to determine a target frequency band signal according to the initial sound signal and extract the target sub-band parameter of the target frequency band signal; A parameter calculation module, configured to perform parameter calculation on the target frequency band signal through a preset frequency band parameter calculation model to obtain the target sub-band energy parameter; A signal filtering module, configured to perform signal filtering on the target frequency band signal through a preset filtering model based on the target sub-band parameter and the target sub-band energy parameter to obtain a target sound signal; The signal filtering module is further configured to acquire an expected signal; update the filtering model coefficient in the preset filtering model according to the target sub-band parameter, the target sub-band energy parameter, and the expected signal to obtain a target filtering model; perform signal filtering on the target frequency band signal through the target filtering model to obtain a target sound signal; The signal filtering module is further configured to acquire current environmental information and determine a corresponding sound attenuation coefficient based on the current environmental information; determine a convergence factor parameter according to the sound attenuation coefficient, the target sub-band energy parameter, and the target sub-band parameter; determine a tap weight vector based on the convergence factor parameter; update the filtering model coefficient in the preset filtering model according to the convergence factor parameter, the expected signal, and the tap weight vector to obtain a target filtering model.

7. A sound quality adjustment device, characterized in that, The sound quality adjustment device includes: a memory, a processor, and a sound quality adjustment program stored on the memory and executable on the processor, where the sound quality adjustment program is configured to implement the sound quality adjustment method according to any one of claims 1 to 5.

8. A storage medium, characterized in that, A sound quality adjustment program is stored on the storage medium, and when the sound quality adjustment program is executed by a processor, it implements the sound quality adjustment method according to any one of claims 1 to 5.

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