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US Patent 10141009 System and method for cluster-based audio event detection

Patent 10141009 was granted and assigned to Pindrop on November, 2018 by the United States Patent and Trademark Office.

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Patent abstractTimelineTable: Further ResourcesReferences
Is a
Patent
Patent
1

Patent attributes

Patent Applicant
Pindrop
Pindrop
1
Current Assignee
Pindrop
Pindrop
1
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
1
Patent Number
101410091
Patent Inventor Names
Elie Khoury1
Matthew Garland1
Date of Patent
November 27, 2018
1
Patent Application Number
156103781
Date Filed
May 31, 2017
1
Patent Citations Received
‌
US Patent 11871190 Separating space-time signals with moving and asynchronous arrays
2
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US Patent 11031017 Fully supervised speaker diarization
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US Patent 11688404 Fully supervised speaker diarization
4
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US Patent 11322171 Parallel signal processing system and method
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US Patent 11355103 Unsupervised keyword spotting and word discovery for fraud analytics
1
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US Patent 11783839 Enrollment and authentication over a phone call in call centers
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US Patent 10930301 Sequence models for audio scene recognition
1
Patent Primary Examiner
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Brian L Albertalli
1
Patent abstract

Methods, systems, and apparatuses for audio event detection, where the determination of a type of sound data is made at the cluster level rather than at the frame level. The techniques provided are thus more robust to the local behavior of features of an audio signal or audio recording. The audio event detection is performed by using Gaussian mixture models (GMMs) to classify each cluster or by extracting an i-vector from each cluster. Each cluster may be classified based on an i-vector classification using a support vector machine or probabilistic linear discriminant analysis. The audio event detection significantly reduces potential smoothing error and avoids any dependency on accurate window-size tuning. Segmentation may be performed using a generalized likelihood ratio and a Bayesian information criterion, and the segments may be clustered using hierarchical agglomerative clustering. Audio frames may be clustered using K-means and GMMs.

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