Abstract: Systems, methods, and apparatus for spectral contrast enhancement of speech signals, based on information from a noise reference that is derived by a spatially selective processing filter from a multichannel sensed audio signal, are disclosed.

Abstract: The present invention relates to blind source separation. More specifically it relates to the blind source separation using frequency domain processes.

Abstract: Systems, methods, and apparatus for processing an M-channel input signal are described that include outputting a signal produced by a selected one among a plurality of spatial separation filters. Applications to separating an acoustic signal from a noisy environment are described, and configurations that may be implemented on a multi-microphone handheld device are also described.

Abstract: Methods, apparatus, and systems for source separation include a converged plurality of coefficient values that is based on each of a plurality of M-channel signals. Each of the plurality of M-channel signals is based on signals produced by M transducers in response to at least one information source and at least one interference source. In some examples, the converged plurality of coefficient values is used to filter an M-channel signal to produce an information output signal and an interference output signal.

Abstract: The system and method for spectral analysis uses a set of spectral data. The spectral data is arranged according to a second dimension, such as time, temperature, position, or other condition. The arranged spectral data is used in a signal separation process, such as an independent component analysis (ICA), which generates independent signals. The independent signals are then used for identifying or quantifying a target component.

Abstract: A method for improving the quality of a speech signal extracted from a noisy acoustic environment is provided. In one approach, a signal separation process is associated with a voice activity detector. The voice activity detector is a two-channel detector, which enables a particularly robust and accurate detection of voice activity. When speech is detected, the voice activity detector generates a control signal. The control signal is used to activate, adjust, or control signal separation processes or post-processing operations to improve the quality of the resulting speech signal. In another approach, a signal separation process is provided as a learning stage and an output stage. The learning stage aggressively adjusts to current acoustic conditions, and passes coefficients to the output stage. The output stage adapts more slowly, and generates a speech-content signal and a noise dominant signal.

Abstract: Methods, apparatus, and systems for source separation include a converged plurality of coefficient values that is based on each of a plurality of M-channel signals. Each of the plurality of M-channel signals is based on signals produced by M transducers in response to at least one information source and at least one interference source. In some examples, the converged plurality of coefficient values is used to filter an M-channel signal to produce an information output signal and an interference output signal.

Abstract: A headset is constructed to generate an acoustically distinct speech signal in a noisy acoustic environment. The headset positions a pair of spaced-apart microphones near a user's mouth. The microphones each receive the user s speech, and also receive acoustic environmental noise. The microphone signals, which have both a noise and information component, are received into a separation process. The separation process generates a speech signal that has a substantial reduced noise component. The speech signal is then processed for transmission. In one example, the transmission process includes sending the speech signal to a local control module using a Bluetooth radio.

Abstract: A system and method for separating a mixture of audio signal into desired audio signals (430) (e.g., speech) and a noise sign (440) is disclosed. Microphones (310, 320) are positioned to receive the mixed audio signals, and an independent component analysis (ICA) processes (212) the sound mixture using stability constraints. The ICA process (508) uses predefined characteristics of the desired speech signal to identify and isolate a target sound signal (430). Filter coefficients are adapted with a learning rule and filter weight update dynamics are stabilized to assist convergence to a stable separated ICA signal result. The separated signals may be peripherally-processed to further reduce noise effects using post-processing (214) and pre-processing (220, 230) techniques and information. The proposed system is designed and easily adaptable for implementation on DSP units or CPUs in audio communication hardware environments.

Abstract: The present invention provides a process for separating a good quality information signal from a noisy acoustic environment. The separation process uses a set of at least two spaced-apart transducers to capture noise and information components. The transducer signals, which have both a noise and information component, are received into a separation process. The separation process generates one channel that is substantially only noise, and another channel that is a combination of noise and information. An identification process is used to identify which channel has the information component. The noise signal is then used to set process characteristics that are applied to the combination signal to efficiently reduce or eliminate the noise component. In this way, the noise is effectively removed from the combination signal to generate a good qualify information signal. The information signal may be, for example, a speech signal, a seismic signal, a sonar signal, or other acoustic signal.

Abstract: The present invention provides a process for separating a good quality information signal from a noisy acoustic environment. The separation process uses a set of at least two spaced-apart transducers to capture noise and information components. The transducer signals, which have both a noise and information component, are received into a separation process. The separation process generates one channel that is substantially only noise, and another channel that is a combination of noise and information. An identification process is used to identify which channel has the information component. The noise signal is then used to set process characteristics that are applied to the combination signal to efficiently reduce or eliminate the noise component. In this way, the noise is effectively removed from the combination signal to generate a good qualify information signal. The information signal may be, for example, a speech signal, a seismic signal, a sonar signal, or other acoustic signal.

Abstract: A method for improving the quality of a speech signal extracted from a noisy acoustic environment is provided. In one approach, a signal separation process is associated with a voice activity detector. The voice activity detector is a two-channel detector, which enables a particularly robust and accurate detection of voice activity. When speech is detected, the voice activity detector generates a control signal. The control signal is used to activate, adjust, or control signal separation processes or post-processing operations to improve the quality of the resulting speech signal. In another approach, a signal separation process is provided as a learning stage and an output stage. The learning stage aggressively adjusts to current acoustic conditions, and passes coefficients to the output stage. The output stage adapts more slowly, and generates a speech-content signal and a noise dominant signal.

Abstract: The present invention provides a process for separating a good quality information signal from a noisy acoustic environment. The separation process uses a set of at least two spaced-apart transducers to capture noise and information components. The transducer signals, which have both a noise and information component, are received into a separation process. The separation process generates one channel that is substantially only noise, and another channel that is a combination of noise and information. An identification process is used to identify which channel has the information component. The noise signal is then used to set process characteristics that are applied to the combination signal to efficiently reduce or eliminate the noise component. In this way, the noise is effectively removed from the combination signal to generate a good qualify information signal. The information signal may be, for example, a speech signal, a seismic signal, a sonar signal, or other acoustic signal.

Abstract: A system and method for separating a mixture of audio signal into desired audio signals (430) (e.g., speech) and a noise sign (440) is disclosed. Microphones (310, 320) are positioned to receive the mixed audio signals, and an independent component analysis (ICA) processes (212) the sound mixture using stability constraints. The ICA process (508) uses predefined characteristics of the desired speech signal to identify and isolate a target sound signal (430). Filter coefficients are adapted with a learning rule and filter weight update dynamics are stabilized to assist convergence to a stable separated ICA signal result. The separated signals may be peripherally-processed to further reduce noise effects using post-processing (214) and pre-processing (220, 230) techniques and information. The proposed system is designed and easily adaptable for implementation on DSP units or CPUs in audio communication hardware environments.

Abstract: The present invention provides a process for separating a good quality information signal from a noisy acoustic environment. The separation process uses a set of a least two spaced-apart transducers to capture noise and information components. The transducer signals, which have both a noise and information component, are received into a separation process. The separation process generates one channel that is substantially only noise, and another channel that is a combination of noise and information. An identification process is used to identify which channel has the information component. The noise signal is then used to set process characteristics that are applied to the combination signal to efficiently reduce or eliminate the noise component. In this way, the noise is effectively removed from the combination signal to generate a good qualify information signal. The information signal may be, for example, a speech signal, a seismic signal, a sonar signal, or other acoustic signal.