- 期刊
Automatic Music Genre Classification Using Modulation Spectral Features and Nonparametric Discriminant Analysis
並列摘要
In this paper, we will propose an automatic music genre classification approach based on long-term modulation spectral analysis of spectral (OSC and MPEG-7 NASE) as well as cepstral (MFCC) features. Modulation spectral analysis of every feature value will generate a corresponding modulation spectrum and all the modulation spectra can be collected to form a modulation spectrogram which exhibits the time-varying or rhythmic information of music signals. Each modulation spectrum is then decomposed into several logarithmically-spaced modulation subbands. A new set of modulation spectral features, including modulation spectral contrast (MSC), modulation spectral valley (MSV), modulation spectral energy (MSE), modulation spectral centroid (MSCEN) and modulation spectral flatness (MSF) are then computed from each modulation subband. Effective and compact features are generated from statistical aggregations of the MSC, MSV, MSE, MSCEN, and MSF features of all modulation subbands. An information fusion approach which integrates both feature level fusion method and decision level combination method is then employed to improve the classification accuracy. Experiments conducted on ISMIR 2004 music dataset have shown that our proposed approach can achieve higher classification accuracy than other approaches with the same experimental setup.
被引用紀錄
延伸閱讀
- Lima, M. F. M., Machado, J. A. T., & Costa, A. C. (2012). A Multidimensional Scaling Analysis of Musical Sounds Based on Pseudo Phase Plane. Abstract and Applied Analysis, 2012(), 383-396-327. https://doi.org/10.1155/2012/436108
- FitzGerald, D., Cranitch, M., & Coyle, E. (2008). Extended Nonnegative Tensor Factorisation Models for Musical Sound Source Separation. Computational Intelligence and Neuroscience, 2008(), 123-137. https://doi.org/10.1155/2008/872425
- 陳致生（2011）。Content-based Automatic Annotation and Preference Learning for Music Information Retrieval〔博士論文，國立清華大學〕。華藝線上圖書館。https://doi.org/10.6843/NTHU.2011.00683
- MEMON, S., BHATTI, S., & ABRO, F. R. (2013). Automatic Speaker Recognition Based on Mel-Frequency Cepstral Coefficients and Gaussian Mixture Models. Mehran University Research Journal of Engineering and Technology, 32(4), 543-550. https://www.airitilibrary.com/Article/Detail?DocID=P20170126001-201310-201702060035-201702060035-543-550
- 林家興（2010）。Discriminative Feature Extraction for Robust Audio Event Classification〔碩士論文，國立臺北科技大學〕。華藝線上圖書館。https://doi.org/10.6841/NTUT.2010.00377