Method for accompaniment purity class evaluation and related devices
View Patent ↗A method for accompaniment purity class evaluation and related devices are provided. Multiple first accompaniment data and a label corresponding to each of the multiple first accompaniment data are obtained, the label being used to indicate that corresponding first accompaniment data is pure instrumental accompaniment data or instrumental accompaniment data with background noise. An audio feature of each of the multiple first accompaniment data is extracted. Model training is performed according to the audio feature of each of the multiple first accompaniment data and the label corresponding to each of the multiple first accompaniment data, to obtain a neural network model for accompaniment purity class evaluation, a model parameter of the neural network model being determined according to an association relationship between the audio feature of each of the multiple first accompaniment data and the label corresponding to each of the multiple first accompaniment data.
1. A method for accompaniment purity class evaluation, comprising:
obtaining a plurality of first accompaniment data and a label corresponding to each of the plurality of first accompaniment data, the label corresponding to each of the plurality of first accompaniment data being used to indicate that corresponding first accompaniment data is pure instrumental accompaniment data or instrumental accompaniment data with background noise;
extracting an audio feature of each of the plurality of first accompaniment data; and
performing model training according to the audio feature of each of the plurality of first accompaniment data and the label corresponding to each of the plurality of first accompaniment data, to obtain a neural network model for accompaniment purity class evaluation, a model parameter of the neural network model being determined according to an association relationship between the audio feature of each of the plurality of first accompaniment data and the label corresponding to each of the plurality of first accompaniment data.
2. The method of claim 1 , further comprising:
before extracting the audio feature of each of the plurality of first accompaniment data,
adjusting each of the plurality of first accompaniment data, to match a playback duration of each of the plurality of first accompaniment data with a preset playback duration; and
normalizing each of the plurality of first accompaniment data, to match a sound intensity of each of the plurality of first accompaniment data with a preset sound intensity.
3. The method of claim 1 , further comprising:
before performing model training according to the audio feature of each of the plurality of first accompaniment data and the label corresponding to each of the plurality of first accompaniment data,
processing the audio feature of each of the plurality of first accompaniment data according to a Z-score algorithm, to standardize the audio feature of each of the plurality of first accompaniment data, the standardized audio feature of each of the plurality of first accompaniment data being matched with a normal distribution.
4. The method of claim 1 , further comprising:
after obtaining the neural network model for accompaniment purity class evaluation,
obtaining an audio feature of each of a plurality of second accompaniment data and a label corresponding to each of the plurality of second accompaniment data;
inputting the audio feature of each of the plurality of second accompaniment data into the neural network model, to obtain an evaluation result of each of the plurality of second accompaniment data;
obtaining an accuracy rate of the neural network model according to a difference between the evaluation result of each of the plurality of second accompaniment data and the label corresponding to each of the plurality of second accompaniment data; and
adjusting the model parameter to retrain the neural network model on condition that the accuracy rate of the neural network model is less than a preset threshold, until the accuracy rate of the neural network model is greater than or equal to the preset threshold and a change magnitude of the model parameter is less than or equal to a preset magnitude.
5. The method of claim 1 , wherein the audio feature comprises any one or any combination of: a mel frequency cepstrum coefficient (MFCC) feature, a relative spectra perceptual linear predictive (RASTA-PLP) feature, a spectral entropy feature, and a perceptual linear predictive (PLP) feature.
6. The method of claim 1 , further comprising:
obtaining data to-be-tested, the data to-be-tested comprising accompaniment data;
extracting an audio feature of the accompaniment data; and
inputting the audio feature into the neural network model, to obtain a purity class evaluation result of the accompaniment data, the evaluation result being used to indicate that the data to-be-tested is pure instrumental accompaniment data or instrumental accompaniment data with background noise.
7. The method of claim 6 , further comprising:
before extracting the audio feature of the accompaniment data,
adjusting the accompaniment data, to match a playback duration of the accompaniment data with a preset playback duration; and
normalizing the accompaniment data, to match a sound intensity of the accompaniment data with a preset sound intensity.
8. The method of claim 6 , further comprising:
before inputting the audio feature into the neural network model,
processing the audio feature of the accompaniment data according to a Z-score algorithm, to standardize the audio feature of the accompaniment data, the standardized audio feature of the accompaniment data being matched with a normal distribution.
9. The method of claim 6 , further comprising:
after obtaining the purity class evaluation result of the accompaniment data,
determining the purity class evaluation result as the pure instrumental accompaniment data on condition that the accompaniment data has purity class greater than or equal to a preset threshold; and
determining the purity class evaluation result as the instrumental accompaniment data with background noise on condition that the data to-be-tested has purity class less than the preset threshold.
10. An electronic device, comprising a processor and a memory, wherein the processor is coupled with the memory, the memory is configured to store computer programs, the computer programs comprise program instructions, and the processor is configured to invoke the program instructions to:
obtain a plurality of first accompaniment data and a label corresponding to each of the plurality of first accompaniment data, the label corresponding to each of the plurality of first accompaniment data being used to indicate that corresponding first accompaniment data is pure instrumental accompaniment data or instrumental accompaniment data with background noise;
extract an audio feature of each of the plurality of first accompaniment data; and
perform model training according to the audio feature of each of the plurality of first accompaniment data and the label corresponding to each of the plurality of first accompaniment data, to obtain a neural network model for accompaniment purity class evaluation, a model parameter of the neural network model being determined according to an association relationship between the audio feature of each of the plurality of first accompaniment data and the label corresponding to each of the plurality of first accompaniment data.
11. The electronic device of claim 10 , wherein the processor is further configured to invoke the program instructions to:
before extracting the audio feature of each of the plurality of first accompaniment data,
adjust each of the plurality of first accompaniment data, to match a playback duration of each of the plurality of first accompaniment data with a preset playback duration; and
normalize each of the plurality of first accompaniment data, to match a sound intensity of each of the plurality of first accompaniment data with a preset sound intensity.
12. The electronic device of claim 10 , wherein the processor is further configured to invoke the program instructions to:
before performing model training according to the audio feature of each of the plurality of first accompaniment data and the label corresponding to each of the plurality of first accompaniment data,
process the audio feature of each of the plurality of first accompaniment data according to a Z-score algorithm, to standardize the audio feature of each of the plurality of first accompaniment data, the standardized audio feature of each of the plurality of first accompaniment data being matched with a normal distribution.
13. The electronic device of claim 10 , wherein the processor is further configured to invoke the program instructions to:
after obtaining the neural network model for accompaniment purity class evaluation,
obtain an audio feature of each of a plurality of second accompaniment data and a label corresponding to each of the plurality of second accompaniment data;
input the audio feature of each of the plurality of second accompaniment data into the neural network model, to obtain an evaluation result of each of the plurality of second accompaniment data;
obtain an accuracy rate of the neural network model according to a difference between the evaluation result of each of the plurality of second accompaniment data and the label corresponding to each of the plurality of second accompaniment data; and
adjust the model parameter to retrain the neural network model on condition that the accuracy rate of the neural network model is less than a preset threshold, until the accuracy rate of the neural network model is greater than or equal to the preset threshold and a change magnitude of the model parameter is less than or equal to a preset magnitude.
14. The electronic device of claim 10 , wherein the audio feature comprises any one or any combination of: a mel frequency cepstrum coefficient (MFCC) feature, a relative spectra perceptual linear predictive (RASTA-PLP) feature, a spectral entropy feature, and a perceptual linear predictive (PLP) feature.
15. The electronic device of claim 10 , wherein the processor is further configured to invoke the program instructions to:
obtain data to-be-tested, the data to-be-tested comprising accompaniment data;
extract an audio feature of the accompaniment data; and
input the audio feature into the neural network model, to obtain a purity class evaluation result of the accompaniment data, the evaluation result being used to indicate that the data to-be-tested is pure instrumental accompaniment data or instrumental accompaniment data with background noise.
16. The electronic device of claim 15 , wherein the processor is further configured to invoke the program instructions to:
before extracting the audio feature of the accompaniment data,
adjust the accompaniment data, to match a playback duration of the accompaniment data with a preset playback duration; and
normalize the accompaniment data, to match a sound intensity of the accompaniment data with a preset sound intensity.
17. The electronic device of claim 15 , wherein the processor is further configured to invoke the program instructions to:
before inputting the audio feature into the neural network model,
process the audio feature of the accompaniment data according to a Z-score algorithm, to standardize the audio feature of the accompaniment data, the standardized audio feature of the accompaniment data being matched with a normal distribution.
18. The electronic device of claim 15 , wherein the processor is further configured to invoke the program instructions to:
after obtaining the purity class evaluation result of the accompaniment data,
determine the purity class evaluation result as the pure instrumental accompaniment data on condition that the accompaniment data has purity class greater than or equal to a preset threshold; and
determine the purity class evaluation result as the instrumental accompaniment data with background noise on condition that the data to-be-tested has purity class less than the preset threshold.
19. A non-transitory computer readable storage medium, wherein the non-transitory computer readable storage medium is configured to store computer programs, the computer programs comprise program instructions which, when executed by a processor, are operable with the processor to:
obtain data to-be-tested, the data to-be-tested comprising accompaniment data;
extract an audio feature of the accompaniment data; and
input the audio feature into a neural network model, to obtain a purity class evaluation result of the accompaniment data, the evaluation result being used to indicate that the data to-be-tested is pure instrumental accompaniment data or instrumental accompaniment data with background noise, the neural network model being obtained through training according to a plurality of samples, the plurality of samples comprising an audio feature of each of a plurality of accompaniment data and a label corresponding to each of the plurality of accompaniment data, a model parameter of the neural network model being determined according to an association relationship between the audio feature of each of the plurality of accompaniment data and the label corresponding to each of the plurality of accompaniment data.
20. The non-transitory computer readable storage medium of claim 19 , wherein the program instructions are further operable with the processor to:
before extracting the audio feature of the accompaniment data,
adjust the accompaniment data, to match a playback duration of the accompaniment data with a preset playback duration; and
normalize the accompaniment data, to match a sound intensity of the accompaniment data with a preset sound intensity.