A sound processing apparatus includes a sound collection position calculating unit configured to calculate sound collection positions of sound signals of multiple channels on the basis of the sound signals, a sound source direction calculating unit configured to calculate a sound source direction on the basis of the sound signals of multiple channels, a first transfer function calculating unit configured to calculate a first transfer function corresponding to the sound source direction on the basis of the sound signals of multiple channels, and a second transfer function calculating unit configured to calculate a second transfer function by interpolating the first transfer function corresponding to each of a plurality of sound source directions.

A sound processing apparatus includes a sound collection position calculating unit configured to calculate sound collection positions of sound signals of multiple channels on the basis of the sound signals, a sound source direction calculating unit configured to calculate a sound source direction on the basis of the sound signals of multiple channels, a first transfer function calculating unit configured to calculate a first transfer function corresponding to the sound source direction on the basis of the sound signals of multiple channels, and a second transfer function calculating unit configured to calculate a second transfer function by interpolating the first transfer function corresponding to each of a plurality of sound source directions.

A radiation ultrasonic wave visualization method in which an ultrasonic wave radiated by a sound source is visualized, comprises: heterodyne-converting ultrasonic signals in a band of at least 20 KHz or more, which are acquired by an ultrasonic sensor array constituted by a plurality of ultrasonic sensors and converting the ultrasonic signals into a low-frequency signal and thereafter, beamforming the converted low-frequency signals or beamforming the converted low-frequency signals based on resampling signals, thereby handling the low-frequency signals without distorting ultrasonic sound location information to reduce a data handling amount in the beamforming step.

A sound classification system for a vehicle includes a plurality of microelectromechanical system (MEMS) microphones disposed at the equipped vehicle and sensing sounds emanating from exterior of the equipped vehicle. A sound processor is operable to process outputs of the MEMS microphones to classify a source of sensed sounds. The sound processor processes the outputs to determine the direction and distance of the source of the sensed sounds relative to the equipped vehicle. The MEMS microphones are one of (i) incorporated in respective ones of a plurality of exterior viewing cameras of the equipped vehicle and (ii) attached at a surface of at least one window of the equipped vehicle.

A method of processing an acoustic image includes the steps of acquiring acoustic signals generated by acoustic sources in a predetermined region of space, generating a multispectral 3D acoustic image that includes a collection of 2D acoustic images, performing a frequency integration of the multispectral acoustic image for generating a 2D acoustic map, locating at least one target acoustic source of interest and modeling the signal spectrum associated with the target acoustic source, generating a classification map obtained by comparing the signal spectrum of each signal associated with each pixel of the multispectral acoustic image and the model of the signal spectrum associated with the target acoustic source to distinguish the spectrum of the signal associated with the target acoustic source from the signal spectra associated with the remaining acoustic sources, and merging the classification map and the acoustic map to obtain a merged map.

A method of processing an acoustic image includes the steps of acquiring acoustic signals generated by acoustic sources in a predetermined region of space, generating a multispectral 3D acoustic image that includes a collection of 2D acoustic images, performing a frequency integration of the multispectral acoustic image for generating a 2D acoustic map, locating at least one target acoustic source of interest and modeling the signal spectrum associated with the target acoustic source, generating a classification map obtained by comparing the signal spectrum of each signal associated with each pixel of the multispectral acoustic image and the model of the signal spectrum associated with the target acoustic source to distinguish the spectrum of the signal associated with the target acoustic source from the signal spectra associated with the remaining acoustic sources, and merging the classification map and the acoustic map to obtain a merged map.

Provided is a direction estimating apparatus using an acoustic sensor, the direction estimating apparatus including a non-directional acoustic sensor, a plurality of directional acoustic sensors provided adjacent to the non-directional acoustic sensor, and a processor configured to obtain a first output signal from the non-directional acoustic sensor and a plurality of second output signals from the plurality of directional acoustic sensors, and estimate a direction of a sound source within an error range from ?5 degrees to +5 degrees by comparing magnitudes between the two output signals and phase information between the first output signal and one of the second output signals.

A method and system for enhancing a target sound signal from multiple sound signals is provided. An array of an arbitrary number of sound sensors positioned in an arbitrary configuration receives the sound signals from multiple disparate sources. The sound signals comprise the target sound signal from a target sound source, and ambient noise signals. A sound source localization unit, an adaptive beamforming unit, and a noise reduction unit are in operative communication with the array of sound sensors. The sound source localization unit estimates a spatial location of the target sound signal from the received sound signals. The adaptive beamforming unit performs adaptive beamforming by steering a directivity pattern of the array of sound sensors in a direction of the spatial location of the target sound signal, thereby enhancing the target sound signal and partially suppressing the ambient noise signals, which are further suppressed by the noise reduction unit.

A method and system for enhancing a target sound signal from multiple sound signals is provided. An array of an arbitrary number of sound sensors positioned in an arbitrary configuration receives the sound signals from multiple disparate sources. The sound signals comprise the target sound signal from a target sound source, and ambient noise signals. A sound source localization unit, an adaptive beamforming unit, and a noise reduction unit are in operative communication with the array of sound sensors. The sound source localization unit estimates a spatial location of the target sound signal from the received sound signals. The adaptive beamforming unit performs adaptive beamforming by steering a directivity pattern of the array of sound sensors in a direction of the spatial location of the target sound signal, thereby enhancing the target sound signal and partially suppressing the ambient noise signals, which are further suppressed by the noise reduction unit.

A system and method is described for generating a confidence level for data generated by a beamforming acoustic beacon system. The system may include an audio emission device to emit a set of sounds corresponding to a set of predefined modal patterns into a listening area. The sounds may be detected by an audio capture device to produce a set of impulse responses corresponding to the modal patterns. The impulse responses may be processed to produce a set of window synthesized impulse responses for various angles. These window synthesized impulse responses may (1) be formed based on a weighted set of the modal patterns that were originally used to emanate sound and (2) seek to emulate a target beam, which is also composed of the same weighted modal patterns. A confidence level may be computed based on the difference between the window synthesized impulse responses and the target beam pattern.