Acoustic scene classification using deep CNN with fine-resolution feature. (1st April 2020)

• Record Type: Journal Article
• Authors: Zhang, Tao; Liang, Jinhua; Ding, Biyun
• Is Part Of: Expert systems with applications. Volume 143(2020)
• Publication Date: 2020-04-01
• Subjects: Acoustic scene classification -- Convolutional neural network -- Lateral construction -- Depth-wise separable convolution -- Fine-resolution convolutional neural network; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2019.113067
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A new model for acoustic scene classification is proposed. A relationship between time-frequency resolution and receptive field is modeled. A fine-resolution feature with semantic information is used for classification. The proposed method can customize feature representations in various resolutions. Compared to other deep CNNs, the proposed model reduces computational complexity. Abstract: Convolutional neural networks with spectrogram feature representation for acoustic scene classification are attracting more and more attentions due to its favorable performance. However, most of the existing methods are still restricted to the tradeoff between the minimum coverage area across time-frequency feature representation, i.e. time-frequency feature resolution, and the depth of CNN models. Thus, it is unfeasible to improve the performance by simply deepening networks. In this paper, fine-resolution convolutional neural network (FRCNN) is proposed to embrace the progress in very deep architecture, feature fusion and convolutional operation. Specifically, lateral construction is applied to generate a fine-resolution feature map with semantic information, and depth-wise separable convolution is utilized to reduce the number of trainable parameters. Extensive experiments demonstrate that the proposed FRCNN exhibits high performance on several metrics, with low computational complexity.