Sparse Coding based Music Genre Classification using Spectro-Temporal Modulations

• Main Authors: Lin, Chih-Shan, 林至善
• Other Authors: 冀泰石
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/11633531873609859029

碩士 === 國立交通大學 === 工學院聲音與音樂創意科技碩士學位學程 === 103 === Music is the spice of human life. In recent years, a research field called Music Information Retrieval (MIR) springs up with advances in technology and needs of listener. Automatic music genre recognition is one of the classical issues in the field. In this thesis, we assume that a specify music instrument with a specific playing style forms a specific spectral pattern on a spectrogram. Then we consider a music spectrogram as the composition of many specify spectral patterns. We believe that the proportion of spectral patterns can be discriminative among music genre. We use short-time Fourier transform spectrogram and spectral-temporal modulation feature as spectral pattern descriptors. These descriptors are represented as the composition of many specify spectral patterns through dictionary learning and sparse coding and used for classifier training. In addition, auditory spectrogram, constant-Q transform spectrogram and corresponding spectral-temporal modulation feature are also used in the experiments. The result shows that systems based on constant-Q transform-based modulation feature performs better than conventional one which usually based on short-time Fourier transform spectrogram.