Abstract: Techniques for predicted audio immersion related to audio capture devices within an audio environment are discussed herein. Examples may include receiving audio stream inputs associated with audio capture devices positioned within one or more audio capture areas of an audio environment, where the audio environment comprises the one or more audio capture areas and one or more non-audio capture areas. Additionally, the audio stream inputs are transformed into respective audio feature sets. The respective audio feature sets are then input to a hybrid neural network model configured to generate respective augmented signal path data vectors for the respective audio stream inputs. Respective neural network paths of the hybrid neural network model may process one or more of the respective audio feature sets based on respective digital signal processing augmentation networks integrated within the hybrid neural network model.

Abstract: Techniques for isolating audio signals related to audio sources within an audio environment are discussed herein. Examples may include receiving a plurality of audio data objects. Each audio data object includes digitized audio signals captured by a capture device positioned within an audio environment. Examples may also include inputting the audio data objects to a source localizer model that is configured to generate, based on the audio data objects, one or more audio source position estimate objects. Examples may also include inputting the audio data objects and each audio source position estimate object to a source generator model of one or more source generator models. The source generator model is configured to generate, based on the audio source position estimate object, a source isolated audio output component. The source isolated audio output component may include isolated audio signals associated with an audio source within the audio environment.

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.