SINGLE-CHANNEL MUSIC SOURCE SEPARATION BY HARMONIC STRUCTURE MODEL AND SUPPORT VECTOR MACHINE

被引:0
作者
Fang J.-T. [1 ]
Yang C.-W. [1 ]
机构
[1] Ming Chuan University, Department of Electronic Engineering, No.5, Deming Rd, Taoyuan City
来源
International Journal of Electrical Engineering | 2022年 / 29卷 / 02期
关键词
harmonic structure; Single-channel source separation; support vector machine;
D O I
10.6329/CIEE.202204_29(2).0003
中图分类号
学科分类号
摘要
Music source separation is an interesting but challenging task. Methods for separation are more difficult if the separation is under a single-channel condition. In principle, each musical instrument has its own characteristic, and a harmonic instrument can gener-ate its unique harmonic structure. Therefore, the harmonic structure model has been adopted for music source separation. In this paper, two mixed music sources are separated by harmonic structure model for each partitioned frame, and then the inter-frames are connected individually by the same harmonic structure. Second, from the average harmonic structure (AHS), these two instruments can be identified by comparing harmonic structures within the database. The frequency and amplitude of these two instruments from the database can be learned by support vector machine (SVM). With these two fea-tures, the inharmonic part of the mixed sources can be separated and classified. Finally, the harmonic part and inharmonic parts are added together for each frame, and the mixed music sources on a single channel can be separated. Experimental results show that the proposed method can improve the performance of signal to distortion ratio (SDR) under single channel music source separation. © 2022, Chinese Institute of Electrical Engineering. All rights reserved.
引用
收藏
页码:43 / 51
页数:8
相关论文
共 50 条
  • [31] AN IMPROVED SUPPORT VECTOR MACHINE MODEL BASED ON WAVECLUSTER
    Deng, Weiguo
    Wang, Li
    Qi, Jing
    Yu, Shan
    Xiang, Tiyan
    ICIM2012: PROCEEDINGS OF THE ELEVENTH INTERNATIONAL CONFERENCE ON INDUSTRIAL MANAGEMENT, 2012, : 514 - 518
  • [32] Model transfer method based on support vector machine
    Xiong Yu-hong
    Wen Zhi-yu
    Liang Yu-qian
    Chen Qin
    Zhang Bo
    Liu Yu
    Xiang Xian-yi
    SPECTROSCOPY AND SPECTRAL ANALYSIS, 2007, 27 (01) : 147 - 150
  • [33] A Modified Support Vector Machine model for Credit Scoring
    Liu X.
    Fu H.
    Lin W.
    International Journal of Computational Intelligence Systems, 2010, 3 (6) : 797 - 803
  • [34] A Support Vector Machine model for currency crises discrimination
    Rocco, CM
    Moreno, JA
    PROCEEDINGS OF THE 6TH JOINT CONFERENCE ON INFORMATION SCIENCES, 2002, : 1123 - 1126
  • [35] A support vector machine model for the situation awareness system
    Liu, Bo
    Lu, Jie
    Zhang, Guangquan
    Hao, Zhifeng
    Gao, Ya
    PROCEEDINGS OF THE 1ST INTERNATIONAL CONFERENCE ON RISK ANALYSIS AND CRISIS RESPONSE, 2007, 2 : 244 - 248
  • [36] Cropland evaluation model based on support vector machine
    Hua, Xiong
    Zou, Lin
    Hao, Xiangping
    Chen, Wei
    2008 PROCEEDINGS OF INFORMATION TECHNOLOGY AND ENVIRONMENTAL SYSTEM SCIENCES: ITESS 2008, VOL 4, 2008, : 44 - 48
  • [37] Support Vector Machine Model of Financial Early Warning
    Chen Hong
    Liu Jingshu
    2011 AASRI CONFERENCE ON APPLIED INFORMATION TECHNOLOGY (AASRI-AIT 2011), VOL 1, 2011, : 45 - 48
  • [38] Online prediction model based on support vector machine
    Wang, Wenjian
    Men, Changqian
    Lu, Weizhen
    NEUROCOMPUTING, 2008, 71 (4-6) : 550 - 558
  • [39] A geometric method for model selection in support vector machine
    Peng, Xinjun
    Wang, Yifei
    EXPERT SYSTEMS WITH APPLICATIONS, 2009, 36 (03) : 5745 - 5749
  • [40] Integrally Private Model Selection for Support Vector Machine
    Kwatra, Saloni
    Varshney, Ayush K.
    Torra, Vicenc C.
    COMPUTER SECURITY. ESORICS 2023 INTERNATIONAL WORKSHOPS, PT I, 2024, 14398 : 249 - 259