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Noise classification method based on BP network

A BP network and noise classification technology, applied in speech analysis, instruments, etc., can solve the problem of low classification accuracy, and achieve the effect of high noise classification accuracy, strong experimentability, and wide applicability

Active Publication Date: 2020-02-21
TIANJIN UNIV
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Problems solved by technology

[0004] However, the noise classification methods proposed so far have low classification accuracy.

Method used

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  • Noise classification method based on BP network
  • Noise classification method based on BP network
  • Noise classification method based on BP network

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[0074] (1) Preprocessing the input noise signal:

[0075] 1. Select data:

[0076] Select Pink, Factory1, F16, Destoryerengine, Buccaneer1; Babble, White, Hfchannel, Factory2, Buccaneer2; Volvo, Machinegun, M109, Leopard, Destoryerops as samples from the Noisex-92 standard noise library, and the sampling frequency is 16KHz. It is divided into three categories as the basis for the classification of the first-level BP network, namely: A1 category: Pink, Factory1, F16, Destoryerengine, Buccaneer1; A2 category: Babble, White, Hfchannel, Factory2, Buccaneer2; A3 category: Volvo, Machinegun , M109, Leopard, Destoryerops.

[0077] 2. Framing and windowing

[0078] (1) Framing: the frame length is 256 points, and the frame shift is 128 points;

[0079] (2) The window function is a Hamming window;

[0080] (2) After preprocessing, there are 36,713 frames of data for each type of noise, and 550,695 frames of data for 15 types of noise. Perform Fourier transform on each frame of dat...

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Abstract

The invention discloses a noise classification method based on a BP network. The method comprises the following steps that an input noise signal is preprocessed; each frame of preprocessed noise signal is subjected to Fourier transform to obtain a noise signal power spectrum; a Mel-frequency cepstral coefficient of each frame of noise signal and first-order difference of the Mel-frequency cepstralcoefficient are calculated by utilizing the power spectrum of each frame of noise signal; a Gammatone frequency cepstral coefficient of each frame of noise signal is calculated; the Mel-frequency cepstral coefficient, the first-order difference of the Mel-frequency cepstral coefficient and the Gammatone frequency cepstral coefficient of each frame of noise signal are combined to serve as a jointfeature of the frame of noise signal, a part of joint features of all frames of noise signal is used as training data, and the other part is used as test data; a primary BP network and a secondary BPnetwork are trained; and the primary BP network and the secondary BP network are jointly tested to obtain a final noise signal classification result. The method has the higher noise classification accuracy.

Description

technical field [0001] The invention relates to a noise classification method. In particular, it relates to a noise classification method based on BP network. Background technique [0002] In the process of speech signal processing, the problem of noise pollution is unavoidable. With the wide application of digital voice signals in scientific research and daily life, the impact of noise on digital voice signals has become more and more obvious. How to effectively suppress noise and improve the quality and intelligibility of voice signals has become a hot topic for many scholars. In speech enhancement technology, a major research difficulty is that there are many sources of noise. The statistical characteristics of different noises are not the same. Therefore, in practical applications, in order to achieve better signal processing effects, it is necessary to treat noises with different noise characteristics differently according to the application. [0003] Generally speak...

Claims

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Application Information

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IPC IPC(8): G10L21/0208G10L21/0216G10L25/24G10L25/27G10L25/30
CPCG10L21/0208G10L21/0216G10L25/27G10L25/24G10L25/30
Inventor 张涛耿彦章邵洋洋
Owner TIANJIN UNIV
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