Environment Sound Classification Using a Two-Stream CNN Based on Decision-Level Fusion
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School of Astronautics, Northwestern Polytechnical University (NPU), 127 Youyi Xilu, Xi’an 710072, China
2
Signals, Images, and Intelligent Systems Laboratory (LISSI / EA 3956), Université Paris-Est, University Paris-Est Creteil, Senart-FB Institute of Technology, 36-37 rue Charpak, 77127 Lieusaint, France
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(7), 1733; https://doi.org/10.3390/s19071733
Received: 13 March 2019
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Revised: 9 April 2019
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Accepted: 9 April 2019
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Published: 11 April 2019
(This article belongs to the Special Issue Novel Modeling, Signal Processing and Machine Learning Techniques for Sensor Data)
With the popularity of using deep learning-based models in various categorization problems and their proven robustness compared to conventional methods, a growing number of researchers have exploited such methods in environment sound classification tasks in recent years. However, the performances of existing models use auditory features like log-mel spectrogram (LM) and mel frequency cepstral coefficient (MFCC), or raw waveform to train deep neural networks for environment sound classification (ESC) are unsatisfactory. In this paper, we first propose two combined features to give a more comprehensive representation of environment sounds Then, a fourfour-layer convolutional neural network (CNN) is presented to improve the performance of ESC with the proposed aggregated features. Finally, the CNN trained with different features are fused using the Dempster–Shafer evidence theory to compose TSCNN-DS model. The experiment results indicate that our combined features with the four-layer CNN are appropriate for environment sound taxonomic problems and dramatically outperform other conventional methods. The proposed TSCNN-DS model achieves a classification accuracy of 97.2%, which is the highest taxonomic accuracy on UrbanSound8K datasets compared to existing models.
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Keywords:
Auditory Cognition; Environment Sound Classification; Convolutional Neural Network; Dempster—Shafer evidence theory; Fusion Model
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MDPI and ACS Style
Su, Y.; Zhang, K.; Wang, J.; Madani, K. Environment Sound Classification Using a Two-Stream CNN Based on Decision-Level Fusion. Sensors 2019, 19, 1733. https://doi.org/10.3390/s19071733
AMA Style
Su Y, Zhang K, Wang J, Madani K. Environment Sound Classification Using a Two-Stream CNN Based on Decision-Level Fusion. Sensors. 2019; 19(7):1733. https://doi.org/10.3390/s19071733
Chicago/Turabian StyleSu, Yu, Ke Zhang, Jingyu Wang, and Kurosh Madani. 2019. "Environment Sound Classification Using a Two-Stream CNN Based on Decision-Level Fusion" Sensors 19, no. 7: 1733. https://doi.org/10.3390/s19071733
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