Method for identifying environment sound events based on time spectrum amplitude scaling vectors

A technology for environmental sound and events, applied in the field of sound event identification, to achieve excellent performance, effective identification and classification

Inactive Publication Date: 2012-11-21
FUZHOU UNIV
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  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

To further complicate the situation, during sound acquisition, the sound event of interest may come from two or more separate sound sources at the same time
Therefore, in the environment of noise and multiple sound sources, the identification of sound events is facing challenges

Method used

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  • Method for identifying environment sound events based on time spectrum amplitude scaling vectors
  • Method for identifying environment sound events based on time spectrum amplitude scaling vectors
  • Method for identifying environment sound events based on time spectrum amplitude scaling vectors

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Embodiment Construction

[0019] The method for identifying environmental sound events based on spectrum-time-amplitude grading vectors in the present invention first calculates the spectrum-time-amplitude grading vectors TSASV of various related sound events as identification prototypes, and stores each identification prototype in a database as a template for distinguishing sound events to be tested Then calculate the TSASV of the sound event to be tested, and compare the TSASV of the sound event to be tested with each identification prototype stored in the database, the prototype sound corresponding to the closest identification prototype of the TSASV of the sound event to be tested Event is the sound event to be recognized.

[0020] The construction method of the time-amplitude grading vector of the spectrum is as follows figure 1 shown, including the following steps:

[0021] Step 1: Perform fast Fourier transform on the acquired environmental sound events to generate a spectrogram;

[0022] Step...

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Abstract

The invention relates to a method for identifying environment sound events based on time spectrum amplitude scaling vectors. Firstly, a time spectrum amplitude scale vector (TSASV) of each related sound event is computed as an identifying prototype, and is stored in a database to be used as a template for identifying a sound event to be detected; and then the TSASV of the sound event to be detected is computed and is compared with each identifying prototype, and the sound event proximate to the identifying prototype is the sound event to be identified. The construction method of the TSASV comprises the steps that fast Fourier transform is carried out on the obtained environment sound event to generate a spectrogram; the generated spectrogram is sampled according to different frequency resolutions, and sub spectrograms with different frequency resolutions are constructed by the spectrogram; signal enhancement is carried out on the sub spectrograms to generate signal-enhanced sub spectrograms; amplitude scaling is carried out on the signal-enhanced sub spectrograms; and the sub spectrograms after amplitude scaling are coded to generate the TSASV. The method is conductive to improving the effectiveness of environment sound event identification.

Description

technical field [0001] The invention relates to the technical field of sound event identification, in particular to a method for identifying environmental sound events based on a time-spectral amplitude scale vector (TSASV). Background technique [0002] Ambient sound recognition attempts to recognize in sound signals the real events hidden within them. It is used in many fields, such as environmental monitoring, acoustic scene analysis and multimedia data retrieval. A general approach to sound event recognition involves extracting recognizable features from audio data and using them as input to a pattern classifier. In sound event recognition, the more effective the features extracted from the sound signal, the better the performance of recognition. [0003] Conventionally, audio signals are characterized as Mayer-Frequency Cepstral Coefficients (MFCCs), or MFCCs combined with MPEG-7 descriptors with Hidden Markov Model (HMM) recognizers. Recently, related research inclu...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G10L15/20G10L15/06
Inventor 李应
Owner FUZHOU UNIV
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