Abstract:
The present invention relates to blind source separation. More specifically it relates to the blind source separation using frequency domain processes.
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 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:
The present invention provides a process (26) for separating a good quality information signal from a noisy acoustic environment (12). The separation process uses a set of a least two spaced-apart transducers (19, 20) to capture noise (13, 15) and information components (14). 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 (30) 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:
Methods, systems and devices enabling a party to a telephone conversation to identify sounds for active filtering so that the identified sound can be actively filtered and/or amplified. Cell phones are provided with a button that allows users to identify sounds for filtering by pressing the button or virtual key when the sound is heard. Sounds recorded in response to such user inputs are processed to identify filtering criteria, such as frequencies and amplitude. The identified filtering criteria are then used to actively filter or enhance sounds. The methods and systems enable user to identify specific sounds for filtering so only those sounds deemed annoying are suppressed while permitting other sounds (e.g., voice) to be heard.
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:
A multi-microphone system performs location-selective processing of an acoustic signal, wherein source location is indicated by directions of arrival relative to microphone pairs at opposite sides of a midsagittal plane of a user's head.
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 (180) is associated with a voice activity detector (185). The voice activity detector (185) is a two-channel (178,182) detector, which enables a particularly robust and accurate detection of voice activity. When a speech is detected, the voice activity detector generates a control signal (411). The control signal (411) is used to activate, adjust, or control signal separation processes or post -processing operations (195) to improve the quality of the resulting speech signal. In another approach, a signal separation process (180) is provided as a learning stage (752) and an output stage (756). The learning stage (752) aggressively adjus to current acoustic conditions and passes coefficients to the output stage (756). The output stage (756) adapts more slowly and generates a speech-content signal (181,770) and a noise dominant signal (407,773). When the learning stage (752) becomes unstable only the learning stage (752) is reset, allowing the output stage (756) to continue outputting a high quality speech 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 (180) is associated with a voice activity detector (185). The voice activity detector (185) is a two-channel (178,182) detector, which enables a particularly robust and accurate detection of voice activity. When a speech is detected, the voice activity detector generates a control signal (411). The control signal (411) is used to activate, adjust, or control signal separation processes or post -processing operations (195) to improve the quality of the resulting speech signal. In another approach, a signal separation process (180) is provided as a learning stage (752) and an output stage (756). The learning stage (752) aggressively adjus to current acoustic conditions and passes coefficients to the output stage (756). The output stage (756) adapts more slowly and generates a speech-content signal (181,770) and a noise dominant signal (407,773). When the learning stage (752) becomes unstable only the learning stage (752) is reset, allowing the output stage (756) to continue outputting a high quality speech signal.