Abstract: Various embodiments of the present disclosure provide methods, apparatus, systems, devices, and/or the like for reducing defects of audio signal samples by using at least one of audio source feature separation machine learning models, audio generation machine learning models, and/or audio source feature classification machine learning models.

Abstract: Techniques for providing an artificial intelligence denoiser related to audio processing are discussed herein. Some embodiments may include providing an audio signal sample associated with at least one microphone to a time-frequency domain transformation pipeline for a transformation period. Some embodiments may include providing the audio signal sample to a deep neural network (DNN) processing loop that is configured to determine a denoiser mask associated with a noise prediction for the audio signal sample. In a circumstance where the denoiser mask is determined prior to expiration of the transformation period, some embodiments may include applying the denoiser mask associated with the noise prediction to a frequency domain version of the audio signal sample associated with the time-frequency domain transformation pipeline to generate a denoised audio signal sample associated with the at least one microphone.