Abstract: An apparatus and method of pre-conditioning audio for machine perception. Machine perception differs from human perception, and different processing parameters are used for machine perception applications (e.g., speech to text processing) as compared to those used for human perception applications (e.g., voice communications). These different parameters may result in pre-conditioned audio that is worsened for human perception yet improved for machine perception.

Abstract: Some disclosed methods involve receiving microphone signals from a microphone system, including signals corresponding to one or more sounds detected by the microphone system. Some methods may involve determining, via a trained neural network, a filtering scheme for the microphone signals, the filtering scheme including one or more filtering processes. The trained neural network may be configured to implement one or more subband-domain adaptive filter management modules. Some methods may involve applying the filtering scheme to the microphone signals, to produce enhanced microphone signals.

Abstract: Some disclosed methods involve receiving audio data of at least a first audio data type and a second audio data type, including audio signals and associated spatial data indicating intended perceived spatial positions for the audio signals, determining at least a first feature type from the audio data and applying a positional encoding process to the audio data, to produce encoded audio data. The encoded audio data may include representations of at least the spatial data and the first feature type in first embedding vectors of an embedding dimension. Some methods may involve training a neural network, based on the encoded audio data, to transform audio data from an input audio data type having an input spatial data type to a transformed audio data type having a transformed spatial data type. Some methods may involve training a neural network to identify an input audio data type.

Abstract: An apparatus and method of pre-conditioning audio for machine perception. Machine perception differs from human perception, and different processing parameters are used for machine perception applications (e.g., speech to text processing) as compared to those used for human perception applications (e.g., voice communications). These different parameters may result in pre-conditioned audio that is worsened for human perception yet improved for machine perception.

Abstract: An apparatus and method of pre-conditioning audio for machine perception. Machine perception differs from human perception, and different processing parameters are used for machine perception applications (e.g., speech to text processing) as compared to those used for human perception applications (e.g., voice communications). These different parameters may result in pre-conditioned audio that is worsened for human perception yet improved for machine perception.

Abstract: Computer-implemented methods for training an acoustic model for a far-field utterance processing system are provided. The acoustic model may be configured to map an input audio signal into linguistic or paralinguistic units. The training may involve imparting far-field acoustic characteristics upon near-field training vectors that include a plurality of near-microphone utterance signals. Imparting the far-field acoustic characteristics may involve generating a plurality of simulated room impulse responses, convolving one or more of the simulated room impulse responses with the near-field training vectors, to produce a plurality of simulated far-field utterance signals and saving the results of the training in one or more non-transitory memory devices corresponding with the acoustic model. Generating simulated room impulse responses may involve simulating room reverberation times but not simulating early reflections from room surfaces.

Abstract: Computer-implemented methods for training an acoustic model for a far-field utterance processing system are provided. The acoustic model may be configured to map an input audio signal into linguistic or paralinguistic units. The training may involve imparting far-field acoustic characteristics upon near-field training vectors that include a plurality of near-microphone utterance signals. Imparting the far-field acoustic characteristics may involve generating a plurality of simulated room impulse responses, convolving one or more of the simulated room impulse responses with the near-field training vectors, to produce a plurality of simulated far-field utterance signals and saving the results of the training in one or more non-transitory memory devices corresponding with the acoustic model. Generating simulated room impulse responses may involve simulating room reverberation times but not simulating early reflections from room surfaces.