Abstract: A noise reduction system and a method of noise reduction includes utilizing an array of microphones to receive sound signals from stationary sound sources and a user that is speaking. Positions of the stationary sound sources relative to the array of microphones are estimated using sound signals emitted from the sound sources at an earlier time. Noise is suppressed in an audio signal based at least in part on the estimated positions of the stationary sound sources.

Abstract: A noise reduction system and a method of noise reduction includes a microphone array comprising a first microphone, a second microphone, and a third microphone. Each microphone has a known position and a known directivity pattern. An instantaneous direction-of-arrival (IDOA) module determines a first phase difference quantity and a second phase difference quantity. The first phase difference quantity is based on phase differences between non-repetitive pairs of input signals received by the first microphone and the second microphone, while the second phase difference quantity is based on phase differences between non-repetitive pairs of input signals received by the first microphone and the third microphone. A spatial noise reduction module computes an estimate of a desired signal based on a priori spatial signal-to-noise ratio and an a posteriori spatial signal-to-noise ratio based on the first and second phase difference quantities.

Abstract: In one embodiment, the present system recognizes a user's speech input using an automatically generated probabilistic context free grammar for street names that maps all pronunciation variations of a street name to a single canonical representation during recognition. A tokenizer expands the representation using position-dependent phonetic tokens and an intersection classifier classifies an intersection, despite the presence of recognition errors and incomplete street names.

Abstract: A first set of signals from an array of one or more microphones, and a second signal from a reference microphone are used to calibrate a set of filter parameters such that the filter parameters minimize a difference between the second signal and a beamformer output signal that is based on the first set of signals. Once calibrated, the filter parameters are used to form a beamformer output signal that is filtered using a non-linear adaptive filter that is adapted based on portions of a signal that do not contain speech, as determined by a speech detection sensor.

Abstract: A method and apparatus are provided that use narrowband data and wideband data to train a wideband acoustic model.

Abstract: A method and apparatus are provided that use narrowband data and wideband data to train a wideband acoustic model.

Abstract: The quality of sound recorded from a plurality of people speaking at the same time is improved by incorporating prior knowledge into an independent component analysis (ICA) separating algorithm. More particularly, prior knowledge is defined as a probability distribution according to some prior situation (e.g., prior distribution of people in a room). A mixture of sounds (e.g., mixture of voices) from a plurality of sources (e.g., people) captured by one or more recording devices (e.g., microphones) is separated into individual components (e.g., individual voices from respective people) by applying an maximum a posteriori (MAP) ICA algorithm which incorporates prior knowledge of the respective sources (e.g., location of sources) directly into the MAP ICA algorithm thereby allowing recovery of independent underlying sounds associated with individual sources from the mixture.

Abstract: Sound signals to be output from a loudspeaker array are modified by a plurality of filters designed according to an unconstrained optimization procedure to improve overall performance (e.g., power, directivity) of the loudspeaker array. More particularly, respective filters are configured to receive a signal to be output to a plurality of loudspeakers. Upon receiving the signal, the respective filters individually modify the received signal according to the results of the unconstrained optimization procedure and then output the individually modified signals to respective loudspeakers. The unconstrained optimization procedure takes into account manufacturing tolerances and individually enhances the signal output to each of a plurality of individual loudspeakers within an array to achieve an overall improvement in performance.

Abstract: A “Sensor Array Post-Filter” provides an adaptive post-filter that accurately models and suppresses both diffuse and directional noise sources as well as interfering speech sources. The post-filter is applied to an output signal produced by a beamformer used to process signals produced by a sensor array. As a result, the Sensor Array Post-Filter operates to improve the signal-to-noise ratio (SNR) of beamformer output signals by providing adaptive post-filtering of the output signals. The post-filter is generated based on a generative statistical model for modeling signal and noise sources at distinct regions in a signal field that considers prior distributions trained to model an instantaneous direction of arrival for signals captured by sensors in the array.

Abstract: A noise reduction system and a method of noise reduction includes a microphone array comprising a first microphone, a second microphone, and a third microphone. Each microphone has a known position and a known directivity pattern. An instantaneous direction-of-arrival (IDOA) module determines a first phase difference quantity and a second phase difference quantity. The first phase difference quantity is based on phase differences between non-repetitive pairs of input signals received by the first microphone and the second microphone, while the second phase difference quantity is based on phase differences between non-repetitive pairs of input signals received by the first microphone and the third microphone. A spatial noise reduction module computes an estimate of a desired signal based on a priori spatial signal-to-noise ratio and an a posteriori spatial signal-to-noise ratio based on the first and second phase difference quantities.

Abstract: A microphone array having at least three microphones provides a captured signal. Spatial noise suppression estimates a desired signal from a captured signal using spatio-temporal distribution of the speech and the noise. In particular, spatial information indicative of at least two quantities of direction are used. A first quantity is based on a first combination of the signals from the at least three microphones, a second quantity is based on a second combination of the signals of the at least three microphones.

Abstract: In one embodiment, the present system recognizes a user's speech input using an automatically generated probabilistic context free grammar for street names that maps all pronunciation variations of a street name to a single canonical representation during recognition. A tokenizer expands the representation using position-dependent phonetic tokens and an intersection classifier classifies an intersection, despite the presence of recognition errors and incomplete street names.

Abstract: A method and apparatus are provided that generate values for a first set of dimensions of a feature vector from a speech signal. The values of the first set of dimensions are used to estimate values for a second set of dimensions of the feature vector to form an extended feature vector. The extended feature vector is then used to train an acoustic model.

Abstract: A “Sensor Array Post-Filter” provides an adaptive post-filter that accurately models and suppresses both diffuse and directional noise sources as well as interfering speech sources. The post-filter is applied to an output signal produced by a beamformer used to process signals produced by a sensor array. As a result, the Sensor Array Post-Filter operates to improve the signal-to-noise ratio (SNR) of beamformer output signals by providing adaptive post-filtering of the output signals. The post-filter is generated based on a generative statistical model for modeling signal and noise sources at distinct regions in a signal field that considers prior distributions trained to model an instantaneous direction of arrival for signals captured by sensors in the array.