# US 12,183,361 B2 · Method and apparatus for recognizing acoustic anomalies > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,183,361 B2 - Document ID: US12183361B2 - Status: Granted - Kind: B2 - Application number: 17/874,072 - Filed: 2022-07-26 - Published: 2022-11-10 - Granted: 2024-12-31 - Earliest claimed: 2020-01-27 - Assignees: Fraunhofer-Gesellschaft zur Foerderung der angewandten Forschung e.V. - Applicants: Fraunhofer-Gesellschaft zur Foerderung der angewandten Forschung e.V. - Inventors: Jakob Abesser - Web page: https://patentgrep.com/patent/US12183361B2 ## CPC - G08B 13/04: by breaking of glass - G10L 25/51: for comparison or discrimination - G08B 13/1672: {using sonic detecting means, e.g. a microphone operating in the audio frequency range} - G08B 21/0469: {Presence detectors to detect unsafe condition, e.g. infrared sensor, microphone (G08B21/0476 takes precedence)} - G08B: SIGNALLING SYSTEMS, e.g. PERSONAL CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS - G10L: SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING ## Abstract A method for detecting anomalies has the following steps: Obtaining a long-term recording having a plurality of first audio segments associated to respective first time windows; analyzing the plurality of the first audio segments to obtain, for each of the plurality of the first audio segments, a first characteristic vector describing the respective first audio segment; obtaining a further recording having one or more second audio segments associated to respective second time windows; analyzing the one or more second audio segments to obtain one or more characteristic vectors describing the one or more second audio segments ABCD; matching the one or more second characteristic vectors with the plurality of the first characteristic vectors to recognize at least one anomaly, like a temporal, sound or spatial anomaly. ## Claims ### Claim 1 A method for recognizing acoustic anomalies, comprising: obtaining a long-term recording having a plurality of first audio segments associated to respective first time windows; analyzing the plurality of the first audio segments to obtain, for each of the plurality of the first audio segments, a first characteristic vector describing the respective first audio segment; wherein the long-term recording comprises at least a duration of 1 minute or 10 minutes or at least 1 hour or at least 24 hours; obtaining a further recording having one or more second audio segments associated to respective second time windows; analyzing the one or more second audio segments to obtain one or more characteristic vectors describing the one or more second audio segments; matching the one or more second characteristic vectors with the plurality of the first characteristic vectors to recognize at least one anomaly when compared to an acoustic normal situation for this environment; and the method: when analyzing, comprising the sub-step of identifying a repetition pattern in the plurality of the first time windows; or comprising recognizing an order of first characteristic vectors belonging to different first audio segments or recognizing an order of groups of identical or similar first characteristic vectors. ### Claim 2 (depends on 1) The method in accordance with claim 1, wherein the anomaly comprises a sound, temporal and/or spatial anomaly; and/or wherein the anomaly comprises a sound anomaly in combination with a temporal anomaly or a sound anomaly in combination with a spatial anomaly or a temporal anomaly in combination with a spatial anomaly. ### Claim 3 (depends on 1) The method in accordance with claim 1, wherein identifying is performed using repeating, identical or similar first characteristic vectors belonging to different first audio segments. ### Claim 4 (depends on 1) The method in accordance with claim 1, wherein, when identifying, grouping of identical or similar first characteristic vectors to form one or more groups is performed. ### Claim 5 (depends on 1) The method in accordance with claim 1, the method comprising identifying a repetition pattern in the one or more second time windows; and/or the method comprising recognizing an order of second characteristic vectors belonging to different second audio segments or recognizing an order of groups of identical or similar second characteristic vectors. ### Claim 6 (depends on 1) The method in accordance with claim 1, the method comprising the sub-step of matching the repetition pattern of the first audio segments and/or order in the first audio segments with the repetition pattern of the second audio segments and/or order in the second audio segments in order to recognize a temporal anomaly. ### Claim 7 (depends on 1) The method in accordance with claim 1, wherein matching comprises the sub-step of identifying a second characteristic vector, which differs from the first characteristic vectors analyzed, in order to recognize a sound anomaly. ### Claim 8 (depends on 1) The method in accordance with claim 1, wherein the characteristic vector comprises one dimension, more dimensions or a reduced dimension space; and/or wherein the method comprises the step of reducing the dimensions of the characteristic vector. ### Claim 9 (depends on 1) The method in accordance with claim 1, the method comprising the step of determining a respective position for the respective first audio segments. ### Claim 10 (depends on 9) The method in accordance with claim 9, the method comprising the step of determining a respective position for the respective second audio segments, and the method comprising the sub-step of matching the position associated to the respective first audio segment with the position associated to the corresponding respective second audio segment in order to recognize a spatial anomaly. ### Claim 11 (depends on 1) The method in accordance with claim 1, the method comprising the step of determining a probability of occurrence of the respective first audio segment and outputting the probability of occurrence with the respective first characteristic vector, or the method comprising the step of determining a probability of occurrence of the respective first audio segment and outputting the probability of occurrence with the respective first characteristic vector and a first time window. ### Claim 12 (depends on 1) The method in accordance with claim 1, wherein the plurality of the first audio segments and/or the plurality of the first audio segments in their order describe an acoustic normal state in the application scenario and/or represent a reference; and/or wherein the one anomaly is recognized when one or more second characteristic vectors deviate from the plurality of the first characteristic vectors. ### Claim 13 (depends on 1) The method in accordance with claim 1, wherein the further recoding comprises a time window or, in particular, a time window of less than 5 minutes, less than 1 minute, or less than 10 seconds. ### Claim 14 A non-transitory digital storage medium having stored thereon a computer program for performing a method for recognizing acoustic anomalies, comprising: obtaining a long-term recording having a plurality of first audio segments associated to respective first time windows, wherein the long-term recording comprises at least a duration of 1 minute or 10 minutes or at least 1 hour or at least 24 hours; analyzing the plurality of the first audio segments to obtain, for each of the plurality of the first audio segments, a first characteristic vector describing the respective first audio segment; obtaining a further recording having one or more second audio segments associated to respective second time windows; analyzing the one or more second audio segments to obtain one or more characteristic vectors describing the one or more second audio segments; matching the one or more second characteristic vectors with the plurality of the first characteristic vectors to recognize at least one anomaly when compared to an acoustic normal situation for this environment; and the method: when analyzing, comprising the sub-step of identifying a repetition pattern in the plurality of the first time windows; or comprising recognizing an order of first characteristic vectors belonging to different first audio segments or recognizing an order of groups of identical or similar first characteristic vectors; when said computer program is run by a computer. ### Claim 15 An apparatus for recognizing acoustic anomalies, comprising: an interface for obtaining a long-term recording having a plurality of first audio segments associated to respective first time windows, wherein the long-term recording comprises at least a duration of 1 minute or 10 minutes or at least 1 hour or at least 24 hours; and for obtaining a further recording having one or more second audio segments associated to respective second time windows; and a processor configured for analyzing the plurality of the first audio segments to obtain, for each of the plurality of the first audio segments, a first characteristic vector describing the respective first audio segment, and configured for analyzing the one or more second audio segments to obtain one or more characteristic vectors describing the one or more second audio segments, and configured for matching the one or more second characteristic vectors with the plurality of the first characteristic vectors to recognize at least one anomaly when compared to an acoustic normal situation for this environment; and said processor: when analyzing, configured for identifying a repetition pattern in the plurality of the first time windows; or configured for recognizing an order of first characteristic vectors belonging to different first audio segments or recognizing an order of groups of identical or similar first characteristic vectors. ### Claim 16 (depends on 15) The apparatus in accordance with claim 15, the apparatus comprising a microphone or a microphone array connected to the interface. ### Claim 17 (depends on 15) The apparatus in accordance with claim 15, the apparatus comprising an output interface for outputting a probability of occurrence of the respective first audio segment having the respective first characteristic vector or for outputting a probability of occurrence of the respective first audio segment having the respective first characteristic vector and a first time window.