Urban noise identification method based on hypercomplex random neural network

A random neural network and urban noise technology, applied in biological neural network models, neural architecture, speech analysis, etc.

Active Publication Date: 2020-08-14
HANGZHOU DIANZI UNIV
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Problems solved by technology

[0004] Aiming at the above-mentioned problems at present, the present invention proposes a quaternion random neural network urban noise recognition method based on super-complex sound signal feature representation

Method used

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  • Urban noise identification method based on hypercomplex random neural network
  • Urban noise identification method based on hypercomplex random neural network
  • Urban noise identification method based on hypercomplex random neural network

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Embodiment Construction

[0078] The present invention will be further described below in conjunction with accompanying drawing and example.

[0079] Such as figure 1 As shown in Fig. 1, all collected signals are first input to the spectral subtraction filter, and the collected signals are filtered through steps such as energy calculation, estimation of non-target sound segments, and spectral subtraction. Such as figure 2 The filtered signal is divided into frames and features are extracted. The resulting filtered signal and corresponding features such as image 3 shown. Such as Figure 4 As shown, the augmented quaternion vector feature after feature extraction and combination is trained by AQ-ELM, and then the number of hidden layer nodes with the optimal classification ability is found, and saved as an urban noise classification model. Finally, the samples to be tested are fed into the trained model to obtain classification results.

[0080] The present invention mainly comprises the followin...

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Abstract

The invention discloses an urban noise identification method based on a hypercomplex random neural network. According to the method, urban noise signals pass through a spectral subtraction filter, then MFCC, LSP and PLP features of the urban noise signals are extracted and spliced into a quaternion augmentation vector, and finally classification and recognition of the noise signals are achieved through a quaternion extreme learning machine (Q-ELM). According to the method, useful information in the signal is fully extracted under the condition of a low signal-to-noise ratio, the augmented quaternion structure can also utilize structural information among features, and the recognition rate of the urban noise signal can be effectively improved.

Description

technical field [0001] The invention belongs to the field of smart city security and intelligent voice recognition, and relates to an urban noise recognition method based on a hypercomplex random neural network. Background technique [0002] Urban noise identification and control is a new issue in the field of urban environmental monitoring and public safety. All kinds of noise accompanying the rapid growth of automobile traffic, building construction and machinery industry have already exceeded the city's acceptable warning line. Therefore, urban environmental noise monitoring and intelligent identification have attracted extensive and high attention in recent years. [0003] For urban noise recognition, predecessors have already listed features such as Mel cepstrum coefficient (MFCC), line spectral pair parameter (LSP) and perceptual linear prediction (PLP) and support vector machine (SVM), decision tree (DT), etc. A recognition algorithm combining classifiers. However,...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G10L21/0232G10L25/24G06N3/04
CPCG10L21/0232G10L25/24G06N3/04
Inventor 曹九稳沈佩婷王建中曾焕强
Owner HANGZHOU DIANZI UNIV
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