公开(公告)号：US20190042881A1

公开(公告)日：2019-02-07

申请号：US15834838

申请日：2017-12-07

Applicant: INTEL CORPORATION

Inventor： Kuba Lopatka ,  Tobias Bocklet ,  Mateusz Kotarski

IPC: G06K9/62 ,  G06F17/18 ,  G06F17/30 ,  G06N3/08

Abstract: Techniques are provided for acoustic event detection. A methodology implementing the techniques according to an embodiment includes extracting acoustic features from a received audio signal. The acoustic features may include, for example, one or more short-term Fourier transform frames, or other spectral energy characteristics, of the audio signal. The method also includes applying a trained classifier to the extracted acoustic features to identify and label acoustic event subparts of the audio signal and to generate scores associated with the subparts. The method further includes performing sequence decoding of the acoustic event subparts and associated scores to detect target acoustic events of interest based on the scores and temporal ordering sequence of the event subparts. The classifier is trained on acoustic event subparts that are generated through unsupervised subspace clustering techniques applied to training data that includes target acoustic events.

公开(公告)号：US10789941B2

公开(公告)日：2020-09-29

申请号：US16146416

申请日：2018-09-28

Applicant: INTEL CORPORATION

Inventor： Kuba Lopatka ,  Mateusz Kotarski ,  Tobias Bocklet ,  Marek Zabkiewicz

IPC: G10L15/16 ,  G10L15/22 ,  G10L15/02 ,  G06N3/04 ,  G06F17/14 ,  G06K9/00 ,  G06K9/62 ,  G10L25/51 ,  G06N3/08 ,  G06F9/54 ,  G06K9/46

Abstract: Techniques are provided for efficient acoustic event detection with reduced resource consumption. A methodology implementing the techniques according to an embodiment includes calculating frames of power spectra based on segments of received acoustic signals. The method further includes two processes, one for detecting impulsive acoustic events and another for detecting continuous acoustic events. The first process includes generating impulsive acoustic event features associated with first and second power spectrum frames, applying a neural network classifier to the impulsive acoustic event features to generate event scores, and detecting an impulsive acoustic event based on those event scores. The second process includes generating reduced-dimension continuous acoustic event features associated with the first and second power spectrum frames, applying a neural network classifier to the reduced-dimension continuous acoustic event features to generate a second set of event scores, and detecting a continuous acoustic event based on the second set of event scores.

公开(公告)号：US11216724B2

公开(公告)日：2022-01-04

申请号：US15834838

申请日：2017-12-07

Applicant: INTEL CORPORATION

Inventor： Kuba Lopatka ,  Tobias Bocklet ,  Mateusz Kotarski

IPC: G06N3/08 ,  G06K9/62 ,  G06F16/683 ,  G10L25/51 ,  G06N20/10 ,  G10L25/18 ,  G06N3/04 ,  G06N7/00

Abstract: Techniques are provided for acoustic event detection. A methodology implementing the techniques according to an embodiment includes extracting acoustic features from a received audio signal. The acoustic features may include, for example, one or more short-term Fourier transform frames, or other spectral energy characteristics, of the audio signal. The method also includes applying a trained classifier to the extracted acoustic features to identify and label acoustic event subparts of the audio signal and to generate scores associated with the subparts. The method further includes performing sequence decoding of the acoustic event subparts and associated scores to detect target acoustic events of interest based on the scores and temporal ordering sequence of the event subparts. The classifier is trained on acoustic event subparts that are generated through unsupervised subspace clustering techniques applied to training data that includes target acoustic events.

公开(公告)号：US20190043489A1

公开(公告)日：2019-02-07

申请号：US16146416

申请日：2018-09-28

Applicant: INTEL CORPORATION

Inventor： Kuba Lopatka ,  Mateusz Kotarski ,  Tobias Bocklet ,  Marek Zabkiewicz

IPC: G10L15/16 ,  G10L15/22 ,  G06F17/14 ,  G10L15/02 ,  G06N3/04 ,  G06F9/54

CPC classification number: G10L15/16 ,  G06F9/542 ,  G06F17/147 ,  G06K9/00536 ,  G06K9/4628 ,  G06K9/627 ,  G06N3/04 ,  G06N3/0454 ,  G06N3/08 ,  G10L15/02 ,  G10L15/22 ,  G10L25/51

Abstract: Techniques are provided for efficient acoustic event detection with reduced resource consumption. A methodology implementing the techniques according to an embodiment includes calculating frames of power spectra based on segments of received acoustic signals. The method further includes two processes, one for detecting impulsive acoustic events and another for detecting continuous acoustic events. The first process includes generating impulsive acoustic event features associated with first and second power spectrum frames, applying a neural network classifier to the impulsive acoustic event features to generate event scores, and detecting an impulsive acoustic event based on those event scores. The second process includes generating reduced-dimension continuous acoustic event features associated with the first and second power spectrum frames, applying a neural network classifier to the reduced-dimension continuous acoustic event features to generate a second set of event scores, and detecting a continuous acoustic event based on the second set of event scores.