Methods and devices for generating sound classifier and detecting abnormal sound, and monitoring system

An abnormal sound and classifier technology, applied in speech analysis, instruments, speech recognition, etc., can solve problems such as abnormal sound and inability to recognize abnormal sound, and achieve the effect of good performance

Active Publication Date: 2013-10-23
CANON KK
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However, such conventional detection methods cannot identify an unk

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  • Methods and devices for generating sound classifier and detecting abnormal sound, and monitoring system
  • Methods and devices for generating sound classifier and detecting abnormal sound, and monitoring system
  • Methods and devices for generating sound classifier and detecting abnormal sound, and monitoring system

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[0026] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

[0027] Please note that similar reference numbers and letters refer to similar items in the figure, so once an item is defined in a figure, it does not need to be discussed in subsequent figures.

[0028] Please also note that in the present disclosure, the terms “first”, “second”, etc. are only used to distinguish units or steps, and are not intended to indicate chronological order, priority, or importance.

[0029] figure 2 It is a block diagram showing an exemplary hardware configuration of a computer system 1000 capable of implementing an embodiment of the present invention.

[0030] Such as figure 2 As shown in the figure, the computer system 1000 includes a computer 1110. The computer 1110 includes a processing unit 1120 connected via a system bus 1121, a system memory 1130, a fixed non-volatile memory interface 1140, a removable non-volatile memory interface ...

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Abstract

The invention relates to methods and devices for generating a sound classifier and detecting an abnormal sound, and a monitoring system. The sound classifier comprises at least one classifier grade. For generating each sound classifier grade, the method for generating the sound classifier comprises the following steps: generating, based on an input sound sample, a normal sound model; calculating, based on the input sound sample and the normal sound model, a first threshold value, wherein the input sound sample is segmented to a first normal sound sample and a first abnormal sound sample according to the normal sound model and the first threshold value; generating, based on a specific abnormal sound sample and the first abnormal sound sample, an abnormal sound model; and calculating, based on the first normal sound sample and the abnormal sound model, a second threshold value, wherein the first normal sound sample is segmented to a second normal sound sample and a second abnormal sound sample according to the abnormal sound model and the second threshold value. The each classifier grade comprises the normal sound model, the first threshold value, the abnormal sound model and the second threshold value.

Description

technical field [0001] The present invention relates to sound detection, and more particularly to methods and apparatus for generating sound classifiers, and methods and detectors for detecting abnormal sounds. Background technique [0002] Abnormal sound detection is important for surveillance systems. The abnormal sound may be any kind of abnormal sound in general situations, such as a specific kind of abnormal sound (for example, a scream, a gunshot, etc.) and an unknown kind of abnormal sound. [0003] Traditional abnormal sound detection methods usually aim to detect specific kinds of abnormal sounds. However, such conventional detection methods cannot identify an unknown kind of abnormal sound, even if the sound is obviously abnormal. [0004] Recently, in document 1 (Akinori Ito et al., Detection of Abnormal Sound Using Multi-stage GMM for Surveillance Microphone, 5th International Conference on Information Assurance and Security, 2009), Akinori Ito et al. proposed ...

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Application Information

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IPC IPC(8): G10L15/02G10L15/14
Inventor 胡伟湘刘贺飞穆向禹
Owner CANON KK
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