Abstract: Disclosed are techniques for performing ray-based acoustic modeling that models scattering of acoustic waves by a surface of a device. The acoustic modeling uses two parameters, a room response representing acoustics and geometry of a room and a device response representing acoustics and geometry of the device. The room response is determined using ray-based acoustic modeling, such as ray tracing. The device response can be measured in an actual environment or simulated and represents an acoustic response of the device to individual acoustic plane waves. The device applies a superposition of the room response and the plane wave scattering from the device response to determine acoustic pressure values and generate microphone audio data. The device can estimate room impulse response (RIR) data using data from the microphones, and can use the RIR data to perform audio processing such as sound equalization, acoustic echo cancellation, audio beamforming, and/or the like.

Abstract: A system configured to improve spatial coverage of output audio and a corresponding user experience by performing upmixing and loudspeaker beamforming to stereo input signals. The system can perform upmixing to the stereo (e.g., two channel) input signal to extract a center channel and generate three-channel audio data. The system may then perform loudspeaker beamforming to the three-channel audio data to enable two loudspeakers to generate output audio having three distinct beams. The user may interpret the three distinct beams as originating from three separate locations, resulting in the user perceiving a wide virtual sound stage despite the loudspeakers being spaced close together on the device.

Abstract: A system configured to improve spatial coverage of output audio and a corresponding user experience by performing upmixing and loudspeaker beamforming to stereo input signals. The system can perform upmixing to the stereo (e.g., two channel) input signal to extract a center channel and generate three-channel audio data. The system may then perform loudspeaker beamforming to the three-channel audio data to enable two loudspeakers to generate output audio having three distinct beams. The user may interpret the three distinct beams as originating from three separate locations, resulting in the user perceiving a wide virtual sound stage despite the loudspeakers being spaced close together on the device.

Abstract: A system configured to improve spatial coverage of output audio and a corresponding user experience by performing upmixing and loudspeaker beamforming to stereo input signals. The system can perform upmixing to the stereo (e.g., two channel) input signal to extract a center channel and generate three-channel audio data. The system may then perform loudspeaker beamforming to the three-channel audio data to enable two loudspeakers to generate output audio having three distinct beams. The user may interpret the three distinct beams as originating from three separate locations, resulting in the user perceiving a wide virtual sound stage despite the loudspeakers being spaced close together on the device.

Abstract: This application describes methods of signal processing and spatial audio synthesis. One such method includes accepting an auditory signal and generating an impression of auditory virtual reality by processing the auditory signal to impute a spatial characteristic on it via convolution with a plurality of head-related impulse responses. The processing is performed in a series of steps, the steps including: performing a first convolution of an auditory signal with a characteristic-independent, mixed-sign filter and performing a second convolution of the result of first convolution with a characteristic-dependent, sparse, non-negative filter. In some described methods, the first convolution can be pre-computed and the second convolution can be performed in real-time, thereby resulting in a reduction of computational complexity in said methods of signal processing and spatial audio synthesis.

Abstract: This application describes methods of signal processing and spatial audio synthesis. One such method includes accepting an auditory signal and generating an impression of auditory virtual reality by processing the auditory signal to impute a spatial characteristic on it via convolution with a plurality of head-related impulse responses. The processing is performed in a series of steps, the steps including: performing a first convolution of an auditory signal with a characteristic-independent, mixed-sign filter and performing a second convolution of the result of first convolution with a characteristic-dependent, sparse, non-negative filter. In some described methods, the first convolution can be pre-computed and the second convolution can be performed in real-time, thereby resulting in a reduction of computational complexity in said methods of signal processing and spatial audio synthesis.

Abstract: This application describes methods of signal processing and spatial audio synthesis. One such method includes accepting an auditory signal and generating an impression of auditory virtual reality by processing the auditory signal to impute a spatial characteristic on it via convolution with a plurality of head-related impulse responses. The processing is performed in a series of steps, the steps including: performing a first convolution of an auditory signal with a characteristic-independent, mixed-sign filter and performing a second convolution of the result of first convolution with a characteristic-dependent, sparse, non-negative filter. In some described methods, the first convolution can be pre-computed and the second convolution can be performed in real-time, thereby resulting in a reduction of computational complexity in said methods of signal processing and spatial audio synthesis.

Abstract: A system for generating and outputting three-dimensional audio data using head-related transfer functions (HRTFs) includes a processor configured to perform operations comprising: using a collection of previously measured HRTFs for audio signals corresponding to multiple directions for at least one subject; performing non-parametric Gaussian process hyper-parameter training on the collection of previously measured HRTFs to generate one or more predicted HRTFs that are different from the previously measured HRTFs; and generating and outputting three-dimensional audio data based on at least the one or more predicted HRTFs.

Abstract: This application describes methods of signal processing and spatial audio synthesis. One such method includes accepting an auditory signal and generating an impression of auditory virtual reality by processing the auditory signal to impute a spatial characteristic on it via convolution with a plurality of head-related impulse responses. The processing is performed in a series of steps, the steps including: performing a first convolution of an auditory signal with a characteristic-independent, mixed-sign filter and performing a second convolution of the result of first convolution with a characteristic-dependent, sparse, non-negative filter. In some described methods, the first convolution can be pre-computed and the second convolution can be performed in real-time, thereby resulting in a reduction of computational complexity in said methods of signal processing and spatial audio synthesis.

Abstract: A system is disclosed for statistical modelling, interpolation, and user-feedback based inference of head-related transfer functions (HRTF) includes a processor performing operations that include using a collection of previously measured head related transfer functions for audio signals corresponding to multiple directions for at least one subject; and performing Gaussian process hyper-parameter training on the collection of audio signals. A method is disclosed for statistical modelling, interpolation, measurement and anthropometry based prediction of head-related transfer functions (HRTF) for a virtual audio system that includes collecting audio signals in transform domain for at least one subject; applying head related transfer functions (HRTF) measurement directions in multiple directions to the collected audio signals; and performing Gaussian hyper-parameter training on the collection of audio signals to generate at least one predicted HRTF.