# US 10,769,204 B2 · System and method for unsupervised discovery of similar audio events > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,769,204 B2 - Document ID: US10769204B2 - Status: Granted - Kind: B2 - Application number: 16/736,901 - Filed: 2020-01-08 - Published: 2020-07-09 - Granted: 2020-09-08 - Earliest claimed: 2019-01-08 - Applicants: Genesys Telecommunications Laboratories, Inc. - Inventors: Rivarol Vergin; Jason Anthony McDowell; Emma Ehrhardt; Adam Paugh - Web page: https://patentgrep.com/patent/US10769204B2 ## CPC - G06F 16/65: Clustering; Classification - G06F 16/2255: {Hash tables} - G06F 16/2272: {Management thereof} - G06F 16/61: Indexing; Data structures therefor; Storage structures - G06F 16/683: using metadata automatically derived from the content - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract A system and method are presented for unsupervised discovery of similar audio events collected from an automated dialing campaign. Locality Sensitive Hashing (LSH) is used to search for similar audio clips within a large dataset of audio recordings. A database is queried for possible matches between an unknown audio clip and any reference carrier audio message present in the database. The database is updated when new, or changed, carrier audio messages are detected. ## Claims ### Claim 1 A method for identifying new carrier audio messages, the method comprising the steps of: determining, via a first process, a presence of one or more groups of similar audio recordings within a dataset of audio recordings, wherein each of the one or more groups of similar audio recordings comprises at least a threshold number of the audio recordings having a predetermined level of like characteristic; and labeling each of the one or more groups of similar audio recordings as a one of the new carrier audio messages; wherein the first process comprises the steps of: for each of the audio recordings in the dataset of audio recordings, separating an associated audio signal into a signaling part and a speech part; segmenting the speech part into frames; computing feature vectors for each of the frames; grouping the feature vectors into overlapping blocks, wherein: each of the overlapping blocks comprises a plurality of the frames; and each of the plurality of frames comprises a plurality of feature vectors; using an algorithm to search the dataset of audio recordings for each of the overlapping blocks, wherein the search comprises: encoding a first one of the overlapping blocks into a binary code, searching the dataset of audio recordings for a hash bucket associated with the binary code, wherein if there is a hash bucket associated with the binary code, identifying the associated hash bucket, and repeating the search until the search is performed for each of the overlapping blocks. ### Claim 2 (depends on 1) The method of claim 1, wherein the signaling part comprises a ring tone and the speech part meets a predetermined threshold of length. ### Claim 3 (depends on 1) The method of claim 1, wherein the feature vectors are invariant to signal degradations. ### Claim 4 (depends on 3) The method of claim 3, wherein the feature vectors comprise Mel Frequency Cepstral Coefficients. ### Claim 5 (depends on 1) The method of claim 1, wherein each of the overlapping blocks contains 10 of the frames, and the 10 of the frames each contain 13 of the feature vectors. ### Claim 6 (depends on 1) The method of claim 1, wherein the each of the one or more groups of similar audio recordings comprises at least 5 of the audio recordings having the predetermined level of like characteristics. ### Claim 7 (depends on 1) The method of claim 1, wherein the algorithm is a locality sensitive hashing algorithm. ### Claim 8 (depends on 1) The method of claim 1, wherein the method further comprises the step of adding the binary code to an existing hash bucket in a database. ### Claim 9 (depends on 1) The method of claim 1, wherein the method further comprises the step of creating a new hash bucket if an existing hash bucket associated with the binary code is not found. ### Claim 10 A system for identifying new carrier audio messages, the system comprising: a processor; and a memory in communication with the processor, the memory storing instructions that, when executed by the processor, causes the processor to determine a presence of one or more groups of similar audio recordings with a dataset of audio recordings, wherein each of the one or more groups of similar audio recordings comprises at least a threshold number of the audio recordings having a predetermined level of like characteristic, and labeling each of the one or more groups of similar audio recordings as a one of the new carrier audio messages by: for each of the audio recordings in the dataset of audio recordings, separating an associated audio signal into a signaling part and a speech part; segmenting the speech part into frames; computing feature vectors for each of the frames; grouping the feature vectors into overlapping blocks, wherein: each of the overlapping blocks comprises a plurality of the frames; and each of the plurality of frames comprises a plurality of feature vectors; using an algorithm to search the dataset of audio recordings for each of the overlapping blocks, wherein the search comprises: encoding a first one of the overlapping blocks into a binary code, searching the dataset of audio recordings for a hash bucket associated with the binary code, wherein if there is a hash bucket associated with the binary code, identifying the associated hash bucket, and repeating the search until the search is performed for each of the overlapping blocks. ### Claim 11 (depends on 10) The system of claim 10, wherein the signaling part comprises a ring tone and the speech part meets a predetermined threshold of length. ### Claim 12 (depends on 10) The system of claim 10, wherein the feature vectors are invariant to signal degradations. ### Claim 13 (depends on 12) The system of claim 12, wherein the feature vectors comprise Mel Frequency Cepstral Coefficients. ### Claim 14 (depends on 10) The system of claim 10, wherein each of the overlapping blocks contains 10 of the frames, and the 10 of the frames each contain 13 of the feature vectors. ### Claim 15 (depends on 10) The system of claim 10, wherein the each of the one or more groups of similar audio recordings comprises at least 5 of the audio recordings having the predetermined level of like characteristics. ### Claim 16 (depends on 10) The system of claim 10, wherein the algorithm is a locality sensitive hashing algorithm. ### Claim 17 (depends on 10) The system of claim 10, wherein the process further comprises adding the binary code to an existing hash bucket in a database. ### Claim 18 (depends on 10) The system of claim 10, wherein the process further comprises creating a new hash bucket if an existing hash bucket associated with the binary code is not found.