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 a directionally dependent acoustic structure for audio processing related to at least one microphone sensor are discussed herein. Examples may include transforming an augmented audio signal defined by a directionally dependent acoustic structure positioned proximate to at least one microphone sensor into at least one audio data object set, inputting the at least one audio data object set to a model configured to generate at least one spatialization data structure indicative of spatialization information for at least one audio source located within the audio environment, and generating audio processing data based at least in part on the at least one spatialization data structure.

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 are disclosed herein for providing dereverberation for audio signals via machine learning and/or user control. Examples may include generating an audio feature set for an audio signal captured via a capture device positioned within an audio environment, inputting the audio feature set to a dereverberation neural network model configured to generate an audio dereverberation mask associated with the audio signal, inputting the audio feature set to a reverberation time estimation model configured to generate reverberation time data associated with the audio signal, and/or generating a dereverberation audio signal based at least in part on the audio dereverberation mask and the reverberation time data.

Abstract: An example immersive audio signal processing system and a computer-implemented method for generating a target arena environment audio stream are provided. The example immersive audio signal processing system includes a plurality of multi-lobe digital sound wave capture devices positioned within the arena environment. The plurality of multi-lobe digital sound wave capture devices is configured to direct first beamformed lobes to a playing region of the arena environment, second beamformed lobes to a spectator region of the arena environment, and third beamformed lobes to a noise source region of the arena environment. A digital signal processor is configured to isolate noise audio components originating from at least the spectator region or the noise source region from the audio signal stream and generate a target arena environment audio stream.

Abstract: Various embodiments of the present disclosure provide methods, apparatuses, systems, and/or devices that are configured to separate multi-source audio signal samples into discrete source separated channel audio samples using trained multi-modal audio source channelization models. Multi-source audio signal samples are difficult to separate because they can include multiple target audio sources (e.g., individual speakers) that are often inter-mixed and overlaid with other audio sources such as noise, music, reverberations, and other audio artifacts. The multi-modal audio source channelization models discussed herein are trained to generate source separated channel audio samples based on audio signal samples and on video signal samples.

Abstract: An example immersive audio signal processing system and a computer-implemented method for generating a target arena environment audio stream are provided. The example immersive audio signal processing system includes a plurality of multi-lobe digital sound wave capture devices positioned within the arena environment. The plurality of multi-lobe digital sound wave capture devices is configured to direct first beamformed lobes to a playing region of the arena environment, second beamformed lobes to a spectator region of the arena environment, and third beamformed lobes to a noise source region of the arena environment. A digital signal processor is configured to isolate noise audio components originating from at least the spectator region or the noise source region from the audio signal stream and generate a target arena environment audio stream.

Abstract: Techniques are disclosed herein for providing front-end audio processing for automatic speech recognition. Examples may include receiving audio data from one or more microphone array devices located within an audio environment, extracting an audio feature set from the audio data, inputting the audio feature set to an audio source separation model to generate an audio speech signal configured for automatic speech recognition (ASR), inputting the audio speech signal to an ASR model configured to generate textual data, and outputting the textual data to a post-processing system.

Abstract: Techniques are disclosed herein for providing audio enhancement and optimization of an immersive audio experience. Examples may include generating an audio feature set for a transduced audio stream captured in an environment, inputting the audio feature set to a neural network model configured to generate an audio isolation mask associated with the transduced audio stream, and generating isolated audio for the transduced audio stream based at least in part on the audio isolation mask.

Abstract: An example immersive audio signal processing system and a computer-implemented method for generating a target arena environment audio stream are provided. The example immersive audio signal processing system includes a plurality of multi-lobe digital sound wave capture devices positioned within the arena environment. The plurality of multi-lobe digital sound wave capture devices is configured to direct first beamformed lobes to a playing region of the arena environment, second beamformed lobes to a spectator region of the arena environment, and third beamformed lobes to a noise source region of the arena environment. A digital signal processor is configured to isolate noise audio components originating from at least the spectator region or the noise source region from the audio signal stream and generate a target arena environment audio stream.

Abstract: Techniques for providing a directionally dependent acoustic structure for audio processing related to at least one microphone sensor are discussed herein. Examples may include transforming an augmented audio signal defined by a directionally dependent acoustic structure positioned proximate to at least one microphone sensor into at least one audio data object set, inputting the at least one audio data object set to a model configured to generate at least one spatialization data structure indicative of spatialization information for at least one audio source located within the audio environment, and generating audio processing data based at least in part on the at least one spatialization data structure.

Abstract: Techniques are disclosed herein for providing beamforming for at least one microphone array based at least in part on a steered response power (SRP) transformation of audio data. Examples may include receiving audio data from multiple audio capture devices comprising at least one microphone array located within an audio environment. Examples may also include generating an SRP transformation of the audio data. The SRP transformation may comprise a set of SRP weights for a spatial coordinate grid representing the audio environment. Examples may also include performing, based at least in part on a signal-to-noise ratio (SNR) estimate associated with the SRP transformation, one or more of beamforming steering or beamforming selection with respect to the at least one linear array microphone.

Abstract: Aspects of the disclosure relate to an audio system architecture designed for sound reinforcement. The audio system may comprise one or more input device(s), one or more output device(s), one or more wireless hubs, and one or more user computing devices. Connections between the input device(s), output device(s), and the one or more wireless hubs may be wireless and automated and/or managed via the one or more user computing devices. The user computing devices or the wireless hub may adjust a configuration of each of the devices in the audio system. One or more of the devices in the audio system may be capable of a network connection which may enable recording, live-streaming to remote audiences, system management and operation, cloud-based storage and/or processing, and more.

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 are disclosed herein for providing full-band audio signal reconstruction enabled by output from a machine learning model trained based on an audio feature set extracted from a portion of the audio signal. Examples may include generating a model input audio feature set for a first frequency portion of an audio signal defined based on a hybrid audio processing frequency threshold. Examples may also include inputting the model input audio feature set to a machine learning model configured to generate a frequency characteristics output related to the first frequency portion of the audio signal. Examples may also include applying the frequency characteristics output to at least a second frequency portion of the audio signal to generate a reconstructed full-band audio signal.

Abstract: An example immersive audio signal processing system and a computer-implemented method for generating a target arena environment audio stream are provided. The example immersive audio signal processing system includes a plurality of multi-lobe digital sound wave capture devices positioned within the arena environment. The plurality of multi-lobe digital sound wave capture devices is configured to direct first beamformed lobes to a playing region of the arena environment, second beamformed lobes to a spectator region of the arena environment, and third beamformed lobes to a noise source region of the arena environment. A digital signal processor is configured to isolate noise audio components originating from at least the spectator region or the noise source region from the audio signal stream and generate a target arena environment audio stream.

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: 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.