Control of speech preservation in speech enhancement

A method of performing denoising on audio signals, comprising:

• determining, by a control system, an aggressiveness control parameter value that modulates a degree of speech preservation to be applied when denoising audio signals;
• obtaining, by the control system, a training set of training samples, a training sample of the training set having a noisy audio signal and a target denoising mask;
• and
• training, by the control system, a machine learning model by:
• (a) generating a frequency domain representation of the noisy audio signal corresponding to the training sample,
• (b) providing the frequency domain representation of the noisy audio signal to the machine learning model,
• (c) generating a predicted denoising mask based on an output of the machine learning model,
• (d) determining a loss representing an error of the predicted denoising mask relative to the target denoising mask corresponding to the training sample,
• (e) updating weights associated with the machine learning model, and
• (f) repeating (a)-(e) until a stopping criterion is reached,
• wherein the trained machine learning model is usable to take, as an input, a noisy test audio signal and generate a corresponding denoised test audio signal, and wherein the aggressiveness control parameter value is used for at least one of:
• 1) generating the frequency domain representation of the noisy audio signals included in the training set;
• 2) modifying the target denoising masks included in the training set;
• 3) Determining an architecture of the machine learning model prior to training the machine learning model;
• or 4) determining the loss,
• wherein the aggressiveness control parameter value is determined based on a type of audio content that is to be processed using the machine learning model.