Abstract: In one aspect, a microphone with closely spaced elements is used to acquire multiple signals from which a signal from a desired source is separated. The signal separation approach uses a combination of direction-of-arrival information or other information determined from variation such as phase, delay, and amplitude among the acquired signals, as well as structural information for the signal from the source of interest and/or for the interfering signals. Through this combination of information, the elements may be spaced more closely than may be effective for conventional beamforming approaches. In some examples, all the microphone elements are integrated into a single a micro-electrical-mechanical system (MEMS).

Abstract: An approach to processing of acoustic signals acquired at a user's device include one or both of acquisition of parallel signals from a set of closely spaced microphones, and use of a multi-tier computing approach in which some processing is performed at the user's device and further processing is performed at one or more server computers in communication with the user's device. The acquired signals are processed using time versus frequency estimates of both energy content as well as direction of arrival. In some examples, a non-negative matrix or tensor factorization approach is used to identify multiple sources each associated with a corresponding direction of arrival of a signal from that source. In some examples, data characterizing direction of arrival information is passed from the user's device to a server computer where direction-based processing is performed.

Abstract: An approach to processing of acoustic signals acquired at a user's device include one or both of acquisition of parallel signals from a set of closely spaced microphones, and use of a multi-tier computing approach in which some processing is performed at the user's device and further processing is performed at one or more server computers in communication with the user's device. The acquired signals are processed using time versus frequency estimates of both energy content as well as direction of arrival. In some examples, a non-negative matrix or tensor factorization approach is used to identify multiple sources each associated with a corresponding direction of arrival of a signal from that source. In some examples, data characterizing direction of arrival information is passed from the user's device to a server computer where direction-based processing is performed.

Abstract: In one aspect, a microphone with closely spaced elements is used to acquire multiple signals from which a signal from a desired source is separated. The signal separation approach uses a combination of direction-of-arrival information or other information determined from variation such as phase, delay, and amplitude among the acquired signals, as well as structural information for the signal from the source of interest and/or for the interfering signals. Through this combination of information, the elements may be spaced more closely than may be effective for conventional beamforming approaches. In some examples, all the microphone elements are integrated into a single a micro-electrical-mechanical system (MEMS).