An acoustic event detection system and a method are provided. The system includes a voice activity detection subsystem, a database, and an acoustic event detection subsystem. The voice activity detection subsystem includes a voice receiving module, a feature extraction module, and a first determination module. The voice receiving module receives an original sound signal, the feature extraction module extracts a plurality of features from the original sound signal, and the first determination module executes a first classification process to determine whether or not the plurality of features match to a start-up voice. The acoustic event detection subsystem includes a second determination module and a function response module. The second determination module executes a second classification process to determine whether the features match to at least one of a plurality of predetermined voices. The function response module executes one of functions corresponding to the predetermined voices that is matched.

A noise cancellation method including generating a first voice signal by canceling a first portion of noise included in an input voice signal using a first network, the first network being a trained u-net structure, and the first portion of the noise being in a time domain, applying a first window to the first voice signal, performing a fast Fourier transform on the first windowed voice signal to acquire a magnitude signal and a phase signal, acquiring a mask using a second network based on the magnitude signal, the second network being another trained u-net structure, applying the mask to the magnitude signal, generating a second voice signal by canceling a second portion of the noise by performing an inverse fast Fourier transform on the first windowed voice signal based on the masked magnitude signal and the phase signal, and applying a second window to the second voice signal.

A noise cancellation method including generating a first voice signal by canceling a first portion of noise included in an input voice signal using a first network, the first network being a trained u-net structure, and the first portion of the noise being in a time domain, applying a first window to the first voice signal, performing a fast Fourier transform on the first windowed voice signal to acquire a magnitude signal and a phase signal, acquiring a mask using a second network based on the magnitude signal, the second network being another trained u-net structure, applying the mask to the magnitude signal, generating a second voice signal by canceling a second portion of the noise by performing an inverse fast Fourier transform on the first windowed voice signal based on the masked magnitude signal and the phase signal, and applying a second window to the second voice signal.

A sound processing apparatus includes a first calculator that calculates first power based on a first signal received by a first microphone that is among the first microphone and a second microphone; a second calculator that calculates second power based on a second signal received by the second microphone; a gain calculator that calculates a gain on the basis of the ratio of the first power to the second power; and a multiplier that processes the second signal using the gain calculated by the gain calculator.

There is disclosed inter alia an apparatus for spatial audio signal encoding comprising means for receiving for each time frequency block of a sub band of an audio frame a spatial audio parameter comprising an azimuth and an elevation; determining a first distortion measure for the audio frame by determining a first distance measure for each time frequency block and summing the first distance measure for each time frequency block; determining a second distortion measure for the audio frame by determining a second distance measure for each time frequency block and summing the second distance measure for each time frequency block, and selecting either the first quantization scheme or the second quantization scheme for quantising the elevation and the azimuth for all time frequency blocks of the sub band of the audio frame, wherein the selecting is dependent on the first and second distortion measures.

There is disclosed inter alia an apparatus for spatial audio signal encoding comprising means for receiving for each time frequency block of a sub band of an audio frame a spatial audio parameter comprising an azimuth and an elevation; determining a first distortion measure for the audio frame by determining a first distance measure for each time frequency block and summing the first distance measure for each time frequency block; determining a second distortion measure for the audio frame by determining a second distance measure for each time frequency block and summing the second distance measure for each time frequency block, and selecting either the first quantization scheme or the second quantization scheme for quantising the elevation and the azimuth for all time frequency blocks of the sub band of the audio frame, wherein the selecting is dependent on the first and second distortion measures.

A method, computer program product, and computing system for generating a three-dimensional model of at least a portion of a three-dimensional space incorporating an ACI system via a video recording subsystem of an ACI calibration platform; and generating one or more audio calibration signals for receipt by an audio recording system included within the ACI system via an audio generation subsystem of the ACI calibration platform.

A method, computer program product, and computing system for generating a three-dimensional model of at least a portion of a three-dimensional space incorporating an ACI system via a video recording subsystem of an ACI calibration platform; and generating one or more audio calibration signals for receipt by an audio recording system included within the ACI system via an audio generation subsystem of the ACI calibration platform.

A segment detecting device according to an embodiment includes at least one memory; and at least one processor. The at least one processor receives at least one of (i) an input signal including a first signal and a second signal or (ii) feature data representing one or a plurality of features of the input signal, estimates a level of the second signal by inputting the input signal or the feature data into a neural network, and determines a segment including the second signal in the input signal based on the level of the second signal.

A segment detecting device according to an embodiment includes at least one memory; and at least one processor. The at least one processor receives at least one of (i) an input signal including a first signal and a second signal or (ii) feature data representing one or a plurality of features of the input signal, estimates a level of the second signal by inputting the input signal or the feature data into a neural network, and determines a segment including the second signal in the input signal based on the level of the second signal.