Deep learning of chroma representation for cover song identification in compression domain

作     者：Fang, Jiunn-Tsair Chang, Yu-Ruey Chang, Pao-Chi 

作者机构：Ming Chuan Univ Dept Elect Engn 5 Deming Rd Taoyuan 33348 Taiwan Natl Cent Univ Dept Commun Engn 300 Jhongda Rd Taoyuan 32001 Taiwan 

出 版 物：《MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING》 (多维系统和信号处理)

年 卷 期：2018年第29卷第3期

页      面：887-902页

核心收录：

学科分类：0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Cover song Music retrieval Sparse autoencoder Descriptor Advanced audio coding 

摘      要：Methods for identifying a cover song typically involve comparing the similarity of chroma features between the query song and another song in the data set. However, considerable time is required for pairwise comparisons. In addition, to save disk space, most songs stored in the data set are in a compressed format. Therefore, to eliminate some decoding procedures, this study extracted music information directly from the modified discrete cosine transform coefficients of advanced audio coding and then mapped these coefficients to 12-dimensional chroma features. The chroma features were segmented to preserve the melodies. Each chroma feature segment was trained and learned by a sparse autoencoder, a deep learning architecture of artificial neural networks. The deep learning procedure was to transform chroma features into an intermediate representation for dimension reduction. Experimental results from a covers80 data set showed that the mean reciprocal rank increased to 0.5 and the matching time was reduced by over 94% compared with traditional approaches.