Urban noise identification method based on deep network migration features and augmented self-encoding

Abstract

The invention discloses an urban noise identification method based on deep network migration features and augmented self-encoding. The method comprises the following steps: 1, preprocessing each typeof collected urban noise signals, including denoising, framing and windowing; 2, converting the processed noise signal into a spectrogram; 3, performing feature extraction on the spectrogram obtainedin the step 2 by using a plurality of pre-trained deep convolutional neural networks; 4, fusing the obtained feature vectors x by using an augmented auto-encoder; 5, on the basis of the fusion features in the step 4, constructing a multilayer one-class classification model; 6, calculating an output weight and a decision threshold value of ML-OCRLS; and 7, carrying out classification prediction onunknown signals. The hidden layer neurons of the augmented auto-encoder provided by the invention can adjust and optimize all features, main information can be extracted based on ML-OCRLS of the augmented auto-encoder, feature redundancy is reduced, and meanwhile, multiple transfer learning features are effectively fused, so that the classification precision of a classifier is improved.

Description

technical field [0001] The invention belongs to the field of sound signal recognition, and relates to an urban noise recognition method based on deep network migration features and augmented self-encoding. Background technique [0002] With the advancement of urbanization, the problem of noise pollution is becoming more and more serious, which has a huge impact on people's quality of life and health. By identifying various typical urban environmental noises and dealing with them accordingly, it plays a vital role in the monitoring and control of urban noise pollution. Most existing methods are based on traditional speech features combined with classifier algorithms for urban noise recognition. However, these methods have the following problems: 1) For urban noise signals with many categories and more complex, traditional speech features cannot effectively represent urban noise signals. 2) The urban noise recognition method based on multiple features alleviates the problem ...

AI Technical Summary

Problems solved by technology

[0003] In order to overcome the problems existing in the above-mentioned urban noise identification, the present invention proposes an urban noise identification method based on deep network migration features and augmented self-encoding

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