文献类型：期刊文献

英文题名：Combining CNN and Broad Learning for Music Classification

作者：Tang, Huan[1];Chen, Ning[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2020

卷号：E103D

期号：3

起止页码：695

外文期刊名：IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS

收录：;EI(收录号：20201208321091);WOS:【SCI-EXPANDED(收录号:WOS:000518370700024)】；

基金：This work was partially supported by the National Natural Science Foundation of China (No. 61771196, 61671156, 61872143).

语种：英文

外文关键词：deep learning; broad learning; random convolutional neural network (RCNN); music classification

摘要：Music classification has been inspired by the remarkable success of deep learning. To enhance efficiency and ensure high performance at the same time, a hybrid architecture that combines deep learning and Broad Learning (BL) is proposed for music classification tasks. At the feature extraction stage, the Random CNN (RCNN) is adopted to analyze the Mel-spectrogram of the input music sound. Compared with conventional CNN, RCNN has more flexible structure to adapt to the variance contained in different types of music. At the prediction stage, the BL technique is introduced to enhance the prediction accuracy and reduce the training time as well. Experimental results on three benchmark datasets (GTZAN, Ballroom, and Emotion) demonstrate that: i) The proposed scheme achieves higher classification accuracy than the deep learning based one, which combines CNN and LSTM, on all three benchmark datasets. ii) Both RCNN and BL contribute to the performance improvement of the proposed scheme. iii) The introduction of BL also helps to enhance the prediction efficiency of the proposed scheme.