Analog Devices,inc.

David Wingate

Noah Stein

Juri Ranieri

Isaac Chase Novet

Johannes Traa

Capacitive sensing can be used to measure electrostatic features of a space. Rudimentary capacitive sensing can be blurry. For instance, the resolution of a capacitive sensor generating a simple electric field is not very high, and the response to the simple electric field is also not very high. Using many capacitive sensors and special sets of excitation signals exciting the capacitive sensors, the capacitive sensors can generate specialized electrostatic fields. Because the specialized electrostatic fields provide different views of the space, enhanced inferences can be made from measurements of responses to those specialized electrostatic fields. Selecting certain specialized electrostatic fields can allow capacitive sensors to sense a focused region of the space. Repeating the steps with varied electrostatic fields can allow capacitive sensors to make enhanced inferences for many focused regions of the space, thereby increasing the resolution of capacitive sensing.

Focused capacitive sensing

Sound waves cause pressure changes in the air, and the pressure changes cause changes in the dielectric constant of air. Capacitive sensor measurements indicative of the changes in the dielectric constant of air can be processed to extract features associated with sound waves in the air. The features can include sound pressure levels represented and recordable as audio samples. Furthermore, the features can help identify types of sounds, determine direction of travel of the sound waves, and/or determine the source location of the audio. Instead of relying on movement of a mechanical member to transduce sound waves through a port into an electrical signal, an improved microphone uses capacitive sensing to directly sample and sense static pressure as well as dynamic pressure or pressure changes in the air to derive audio samples. The resulting microphone avoids disadvantages of the conventional microphone having the moving mechanical member and port.

Portless and membrane-free microphone

The present disclosure relates generally to improving audio processing using an intelligent microphone and, more particularly, to techniques for processing audio received at a microphone with integrated analog-to-digital conversion, digital signal processing, acoustic source separation, and for further processing by a speech recognition system. Embodiments of the present disclosure include intelligent microphone systems designed to collect and process high-quality audio input efficiently. Systems and method for audio processing using an intelligent microphone include an integrated package with one or more microphones, analog-to-digital converters (ADCs), digital signal processors (DSPs), source separation modules, memory, and automatic speech recognition. Systems and methods are also provided for audio processing using an intelligent microphone that includes a microphone array and uses a preprogrammed audio beamformer calibrated to the included microphone array.

Audio processing using an intelligent microphone

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).

Signal source separation

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.

Time-frequency directional processing of audio signals

The disclosed apparatus, systems, and methods provide a calibration technique for calibrating a set of microphones. The disclosed calibration technique is configured to calibrate the microphones with respect to a reference microphone and can be used in actual operation rather than a testing environment. The disclosed calibration technique can estimate both the magnitude calibration factor for compensating magnitude sensitivity variations and the relative phase error for compensating phase delay variations. In addition, the disclosed calibration technique can be used even when multiple acoustic sources are present. The disclosed technique is particularly well suited to calibrating a set of microphones that are omnidirectional and sufficiently close to one another.

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Ashland, MA, United States of America

David Wingate