Audio signal processing method and apparatus, electronic device, and storage medium with machine learning and for microphone mute state features in a multi person voice call

An audio signal processing apparatus, disposed in a terminal, the apparatus comprising:

• at least one memory configured to store program code;
• and
• at least one processor configured to read the program code and operate as instructed by the program code, the program code comprising:
• first obtaining code, configured to cause the at least one processor to obtain an audio signal acquired by an application while an account logging into the application is in a microphone mute state in a multi-person voice call;
• second obtaining code, configured to cause the at least one processor to obtain gain parameters, for each of a plurality of audio frames in the audio signal, respectively on a plurality of bands in a first band range, and the gain parameters are obtained by inputting data of the audio signal into a noise suppression (NS) model of a recurrent neural network (RNN)I;
• and
• output code, configured to cause the at least one processor to output a prompt message responsive to a determination, based on the gain parameters, that a target voice is contained in the audio signal, the prompt message providing a prompt to disable the microphone mute state of the account,
• wherein the gain parameters are obtained by:
• preprocessing the audio signal to obtain a first signal, the first signal comprising the data of the audio signal;
• and
• inputting a plurality of audio frames in the first signal into the noise suppression (NS) model, and processing each audio frame in the plurality of audio frames by the NS model to thereby output a gain parameter of each audio frame on each band in the first band range, the gain parameter of the audio frame on a human voice band being greater than the gain parameter on a noise band,
• wherein the RNN comprises at least one hidden layer, each hidden layer comprises a plurality of neurons, and the number of neurons in each hidden layer is equal to the number of inputted audio frames, and
• the NS model processes each audio frame by:
• weighting, through a present neuron in a present hidden layer in the RNN, a first frequency feature outputted by a previous neuron in the present hidden layer and a second frequency feature outputted by a neuron at a corresponding position in a previous hidden layer, and
• inputting the weighted first and second frequency features to a next neuron in the present hidden layer and a neuron at a corresponding position in a next hidden layer respectively.

A non-transitory computer-readable storage medium, the storage medium storing at least one computer program, the at least one computer program being executable by a processor to perform audio signal processing operations of:

• obtaining an audio signal acquired by an application while an account logging into the application is in a microphone mute state in a multi-person voice call;
• obtaining gain parameters, for each of a plurality of audio frames in the audio signal, respectively on a plurality of bands in a first band range, and the gain parameters are obtained by inputting data of the audio signal into a noise suppression (NS) model of a recurrent neural network (RNN);
• and
• outputting a prompt message responsive to a determination, based on the gain parameters, that a target voice is contained in the audio signal, the prompt message providing a prompt to disable the microphone mute state of the account,
• wherein the gain parameters are obtained by:
• preprocessing the audio signal to obtain a first signal, the first signal comprising the data of the audio signal;
• and
• inputting a plurality of audio frames in the first signal into the noise suppression (NS) model, and processing each audio frame in the plurality of audio frames by the NS model to thereby output a gain parameter of each audio frame on each band in the first band range, the gain parameter of the audio frame on a human voice band being greater than the gain parameter on a noise band,
• wherein the RNN comprises at least one hidden layer, each hidden layer comprises a plurality of neurons, and the number of neurons in each hidden layer is equal to the number of inputted audio frames, and
• the NS model processes each audio frame by:
• weighting, through a present neuron in a present hidden layer in the RNN, a first frequency feature outputted by a previous neuron in the present hidden layer and a second frequency feature outputted by a neuron at a corresponding position in a previous hidden layer, and
• inputting the weighted first and second frequency features to a next neuron in the present hidden layer and a neuron at a corresponding position in a next hidden layer respectively.