# US 12,112,750 B2 · Acoustic zoning with distributed microphones > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,112,750 B2 - Document ID: US12112750B2 - Status: Granted - Kind: B2 - Application number: 17/630,895 - Filed: 2020-07-28 - Published: 2022-10-20 - Granted: 2024-10-08 - Earliest claimed: 2019-07-30 - Assignees: Dolby Laboratories Licensing Corporation - Applicants: Dolby Laboratories Licensing Corporation - Inventors: Mark R. P. Thomas; Richard J. Cartwright - Web page: https://patentgrep.com/patent/US12112750B2 ## CPC - G10L 15/22: Procedures used during a speech recognition process, e.g. man-machine dialogue - G06F 3/167: {Audio in a user interface, e.g. using voice commands for navigating, audio feedback} - G10L 15/08: Speech classification or search - G10L 21/0216: characterised by the method used for estimating noise - G10L 21/0264: characterised by the type of parameter measurement, e.g. correlation techniques, zero crossing techniques or predictive techniques - H04R 1/406: {microphones} - H04R 3/005: {for combining the signals of two or more microphones (specially adapted for hearing aids H04R25/407)} - H04S 7/303: {Tracking of listener position or orientation} - G10L 2015/088: {Word spotting} - G10L 2015/223: {Execution procedure of a spoken command} - G10L 2021/02166: {Microphone arrays; Beamforming} - H04R 1/028: {associated with devices performing functions other than acoustics, e.g. electric candles} - H04R 3/12: for distributing signals to two or more loudspeakers {(specially adapted for hearing aids H04R25/407)} - H04R 27/00: Public address systems (circuits for preventing acoustic reaction H04R3/02; circuits for distributing signals to loudspeakers H04R3/12; {monitoring or testing arrangements for public address systems H04R29/007}; amplifiers H03F) - H04R 2201/405: Non-uniform arrays of transducers or a plurality of uniform arrays with different transducer spacing - H04R 2227/005: Audio distribution systems for home, i.e. multi-room use - H04R 2430/21: Direction finding using differential microphone array [DMA] - G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - H04R: LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; ELECTRIC HEARING AIDS; PUBLIC ADDRESS SYSTEMS (producing sounds with frequency not determined by supply frequency G10K) - H04S: STEREOPHONIC SYSTEMS ## Abstract A method for estimating a user's location in an environment may involve receiving output signals from each microphone of a plurality of microphones in the environment. At least two microphones of the plurality of microphones may be included in separate devices at separate locations in the environment and the output signals may correspond to a current utterance of a user. The method may involve determining multiple current acoustic features from the output signals of each microphone and applying a classifier to the multiple current acoustic features. Applying the classifier may involve applying a model trained on previously-determined acoustic features derived from a plurality of previous utterances made by the user in a plurality of user zones in the environment. The method may involve determining, based at least in part on output from the classifier, an estimate of the user zone in which the user is currently located. ## Claims ### Claim 1 A computer-implemented method for estimating a user's location in an environment, the method comprising: receiving output signals from each microphone of a plurality of microphones in the environment, at least two microphones of the plurality of microphones being included in separate devices at separate locations in the environment, the output signals corresponding to a current utterance of a user; determining multiple current acoustic features from the output signals of each microphone; and applying a classifier to the multiple current acoustic features, wherein applying the classifier involves applying a model trained to correlate the plurality of user zones in the environment and the multiple acoustic features, on previously-determined acoustic features determined from the output signals of each microphone corresponding to a plurality of previous utterances made by the user in a plurality of user zones in the environment, wherein the output of the classifier provides an estimate of the user zone in which the user is currently located, wherein a first microphone of the plurality of microphones samples audio data according to a first sample clock and a second microphone of the plurality of microphones samples audio data according to a second sample clock, and wherein the current utterance and the previous utterances each comprise an utterance of a same wakeword and the acoustic features include a wakeword duration metric. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein at least one of the plurality of microphones is included in or configured for communication with a smart audio device. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, wherein the plurality of user zones comprises a plurality of predetermined user zones. ### Claim 4 (depends on 1) The computer-implemented method of claim 1, wherein the estimate is determined without reference to geometric locations of the plurality of microphones. ### Claim 5 (depends on 1) The computer-implemented method of claim 1, wherein the multiple current acoustic features are determined asynchronously. ### Claim 6 (depends on 1) The computer-implemented method of claim 1, wherein the classifier estimates a posterior probability for each user zone wherein the user zone in which the user is currently located is estimated as the user zone with maximum posterior probability. ### Claim 7 (depends on 1) The computer-implemented method of claim 1, wherein the model is trained using training data that is labelled with user zones. ### Claim 8 (depends on 1) The computer-implemented method of claim 1, wherein the model is trained using unlabelled training data that is not labelled with user zones. ### Claim 9 (depends on 1) The computer-implemented method of claim 1, wherein the acoustic features further include at least one received level metric, wherein the received level metric indicates a sound level detected by a microphone. ### Claim 10 (depends on 9) The computer-implemented method of claim 9, wherein the model is a Gaussian Mixture Model trained on one or more of normalized wakeword confidence, normalized mean received level, or maximum received level. ### Claim 11 (depends on 10) The computer-implemented method of claim 10, wherein the normalized mean received level includes a mean received level over a duration of the most confident wakeword and/or the maximum received level comprises a maximum received level over a duration of the most confident wakeword. ### Claim 12 (depends on 1) The computer-implemented method of claim 1, wherein training of the model continues during a process of applying the classifier. ### Claim 13 (depends on 12) The computer-implemented method of claim 12, wherein the training is based on explicit feedback from the user. ### Claim 14 (depends on 12) The computer-implemented method of claim 12, wherein the training is based on implicit feedback to the success of beamforming or microphone selection based on an estimated user zone. ### Claim 15 (depends on 14) The computer-implemented method of claim 14, wherein the implicit feedback includes at least one of: a determination that a user has terminated the response of a voice assistant abnormally; or a command recognizer returning a low-confidence result; or a second-pass retrospective wakeword detector returning low confidence that the wakeword was spoken. ### Claim 16 (depends on 1) The computer-implemented method of claim 1, further comprising selecting at least one speaker according to the estimated user zone and controlling the at least one speaker to provide sound to the estimated user zone. ### Claim 17 (depends on 1) The computer-implemented method of claim 1, further comprising selecting at least one microphone according to the estimated user zone and providing signals output by the at least one microphone to a smart audio device. ### Claim 18 An apparatus, comprising: an interface system configured for receiving output signals from each microphone of a plurality of microphones in an environment, at least two microphones of the plurality of microphones being included in separate devices at separate locations in the environment, the output signals corresponding to a current utterance of a user; and a control system configured for: determining multiple current acoustic features from the output signals of each microphone; applying a classifier to the multiple current acoustic features, wherein applying the classifier involves applying a model trained to correlate the plurality of user zones in the environment and the multiple acoustic features, on previously-determined acoustic features determined from the output signals of each microphone corresponding to a plurality of previous utterances made by the user in a plurality of user zones in the environment, wherein the output of the classifier provides an estimate of the user zone in which the user is currently located, wherein the classifier estimates a posterior probability for each user zone, wherein the user zone in which the user is currently located is estimated as the user zone with maximum posterior probability, and wherein the current utterance and the previous utterances each comprise an utterance of a same wakeword and the acoustic features include a wakeword duration metric. ### Claim 19 (depends on 1) A system configured to perform the method of claim 1. ### Claim 20 (depends on 1) One or more non-transitory media having software stored thereon, the software including instructions for controlling one or more devices to perform the method of claim 1.