Acoustic scene identification method based on data enhancement

Abstract

The invention discloses an acoustic scene identification method based on data enhancement. The method comprises the following steps: firstly, collecting and marking audio samples of different sound scenes; then preprocessing is carried out, and pre-emphasis, framing and windowing processing are carried out on the audio samples; data enhancement is then performed, extracting a harmonic source and an impact source of each audio sample to obtain more sufficient audio samples, extracting logarithmic Mel filter bank characteristics from the audio samples and the harmonic sources and the impact sources of the audio samples, stacking the three characteristics into a three-channel high-dimensional characteristic, and constructing more abundant training samples by adopting a hybrid enhancement technology; and finally, inputting the three-channel high-dimensional features into an Xception network for judgment, and identifying the sound scene corresponding to each audio sample. According to the data enhancement method, the generalization capability of the Xception network classifier can be effectively improved, and the training process of the network is stabilized. When the acoustic scene isidentified, the method can obtain a better identification effect.

Description

technical field [0001] The present invention relates to the technical fields of audio signal processing and deep learning, in particular to a sound scene recognition method based on data enhancement. Background technique [0002] Audio signals contain rich information and have the advantages of being non-contact and natural. A sound scene is a high-level representation of an audio signal at the semantic level. The task of acoustic scene recognition is to associate semantic labels with audio streams to identify categories of sound-producing environments. This technology enables smart devices to perceive the surrounding environment based on sound, so as to make appropriate decisions. At present, there is a massive increase in audio data. Due to the time-consuming and labor-intensive manual labeling of data, there are very few audio samples with accurate labels. Unlabeled audio samples cannot be directly used to train a classifier. How to construct more diverse training dat...

AI Technical Summary

Problems solved by technology

At present, manual labeling is mainly used to increase the diversity of labeled data, but the cost of manual labeling is very high

In addition, in different classification tasks, data samples need to be relabeled, which is not universal, making the cost of manual labeling even higher

In order to overcome the problems of high cost of manually labeled data and insufficient labeled training data, data enhancement methods are urgently needed to increase the diversity of limited labeled audio data, thereby improving the adaptability and generalization ability of complex classifiers

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