Abstract: A spatial-audio recording system includes a spatial-audio recording device including a plurality of microphones, and a computing device. The computing device is configured to determine a plane-wave transfer function for the spatial-audio recording device based on a physical shape of the spatial-audio recording device and to expand the plane-wave transfer function to generate a spherical-harmonics transfer function corresponding to the plane-wave transfer function. The computing device is further configured to retrieve a plurality of signals captured by the microphones, determine spherical-harmonics coefficients for an audio signal based on the plurality of captured signals and the spherical-harmonics transfer function, and generate the audio signal based on the determined spherical-harmonics coefficients.

Abstract: A spatial-audio recording system includes a spatial-audio recording device including a plurality of microphones, and a computing device. The computing device is configured to determine a plane-wave transfer function for the spatial-audio recording device based on a physical shape of the spatial-audio recording device and to expand the plane-wave transfer function to generate a spherical-harmonics transfer function corresponding to the plane-wave transfer function. The computing device is further configured to retrieve a plurality of signals captured by the microphones, determine spherical-harmonics coefficients for an audio signal based on the plurality of captured signals and the spherical-harmonics transfer function, and generate the audio signal based on the determined spherical-harmonics coefficients.

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: The dereverberation of signals in reverberating environments is carried out via acquiring the representation (image) of spatial distribution of the signals in space of interest and automatic identification of reflections of the source signal in the reverberative space. The technique relies on identification of prominent features at the image, as well as corresponding directions of propagation of signals manifested by the prominent features at the image, and computation of similarity metric between signals corresponding to the prominent features in the image. The time delays between the correlated signals (i.e., source signal and related reflections) are found and the signals are added coherently. Multiple beamformers operate on the source signal and corresponding reflections, enabling one to improve the signal-to-noise ratio in multi-path environments.

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.

Abstract: Methods and devices for sound detection and audio control are provided. A listening device (100) can include a receiver (102) and a sound director for directing a sound produced by the receiver into an ear of the user, a microphone (104) and a mount for mounting the microphone so as to receive the sound in an environment, a detector for detecting an auditory signal in the sound received by the microphone, and an alerting device for alerting the user to the presence of the auditory signal. The user's personal safety is enhanced due to the user being alerted to the presence of the auditory signal, which otherwise may be unnoticed by the user due to a loud sound level created at the ear of the user by the receiver.

Abstract: The dereverberation of signals in reverberating environments is carried out via acquiring the representation (image) of spatial distribution of the signals in space of interest and automatic identification of reflections of the source signal in the reverberative space. The technique relies on identification of prominent features at the image, as well as corresponding directions of propagation of signals manifested by the prominent features at the image, and computation of similarity metric between signals corresponding to the prominent features in the image. The time delays between the correlated signals (i.e., source signal and related reflections) are found and the signals are added coherently. Multiple beamformers operate on the source signal and corresponding reflections, enabling one to improve the signal-to-noise ratio in multi-path environments.

Abstract: At least one exemplary embodiment is directed to a listening device (100) can include a receiver (102) and means for directing a sound produced by the receiver into an ear of the user, a microphone (104) and means for mounting the microphone so as to receive the sound in an environment, detecting means for detecting an auditory signal in the sound received by the microphone, and alerting means for alerting the user to the presence of the auditory signal, whereby the user's personal safety is enhanced due to the user being alerted to the presence of the auditory signal, which otherwise may be unnoticed by the user due to loud sound level created at the ear of the user by the receiver.