Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (14): 181-186.DOI: 10.3778/j.issn.1002-8331.2005-0144

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Sparse Feature Extraction Method for Mixed Instruments Music Analysis

YUE Qi, XU Zhongliang, GUO Jifeng   

  1. College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
  • Online:2021-07-15 Published:2021-07-14



  1. 东北林业大学 信息与计算机工程学院,哈尔滨 150040


In the research of instrument recognition and analysis of mixed instrument music data, the existing methods rely heavily on data labels, while are often based on simple frequency domain or physical characteristics, which are not obviously related to the inherent properties of the instrument, and lack of sensitivity to complex components. This paper proposes a sparse feature extraction method based on sparse decomposition and multiple instrument component dictionaries, which can get sparse features that will be used independently & with high interpretability through in-depth analysis of the sparse coefficient vector. Experimental result shows that these features can express the composition of musical instruments and the changes of musical mood directly, and also shows significant application value in the field of composition analysis of mixed musical instruments and other kinds of time-varying signal analysis.

Key words: feature extraction, sparse decomposition, sparse feature, mixed instrument recognition, music time domain analysis



关键词: 特征提取, 稀疏分解, 稀疏特征, 混合乐器识别, 音乐时域分析