An Audio Data Representation for Traffic Acoustic Scene Recognition

• Published in: IEEE access Vol. 8; pp. 177863 - 177873
• Main Authors: Jiang, Dazhi, Huang, Dongmin, Song, Youyi, Wu, Kaichao, Lu, Huakang, Liu, Quanquan, Zhou, Teng
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: acoustic material; acoustic scene recognition; Acoustics; Audio data; Audio signals; Feature extraction; Histograms; Recognition; Representations; Spectrogram; Time-frequency analysis; Traffic information; Transforms; transportation; Visual perception; Visualization
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3027474

Acoustic scene recognition (ASR), recognizing acoustic environments given an audio recording of the scene, has a wide range of applications, e.g. robotic navigation and audio forensic. However, ASR remains challenging mainly due to the difficulty of representing audio data. In this article, we focus on traffic acoustic data. Traffic acoustic sense recognition provides complementary information to visual information of the scene; for example, it can be used to verify the visual perception result. The acoustic analysis and recognition, in consideration of its simple and convenient, can effectively enhance the perception ability which only applies visual information. We propose an audio data representation method to improve the traffic acoustic scene recognition accuracy. The proposed method employs the constant Q transform (CQT) and histogram of gradient (HOG) to transfer the one-dimensional audio signals into a time-frequency representation. We also propose two data representation mechanisms, called global and local feature selections, in order to select features that are able to describe the shape of time-frequency structures. We finally exploit the least absolute shrinkage and selection operator (LASSO) technique to further improve the recognition accuracy, by further selecting the most representative information for the recognition. We implemented extensive experiments, and the results show that the proposed method is effective, significantly outperforming the state-of-the-art methods.