- Research Article
- Open Access
Music Genre Classification Using MIDI and Audio Features
EURASIP Journal on Advances in Signal Processing volume 2007, Article number: 036409 (2007)
We report our findings on using MIDI files and audio features from MIDI, separately and combined together, for MIDI music genre classification. We use McKay and Fujinaga's 3-root and 9-leaf genre data set. In order to compute distances between MIDI pieces, we use normalized compression distance (NCD). NCD uses the compressed length of a string as an approximation to its Kolmogorov complexity and has previously been used for music genre and composer clustering. We convert the MIDI pieces to audio and then use the audio features to train different classifiers. MIDI and audio from MIDI classifiers alone achieve much smaller accuracies than those reported by McKay and Fujinaga who used not NCD but a number of domain-based MIDI features for their classification. Combining MIDI and audio from MIDI classifiers improves accuracy and gets closer to, but still worse, accuracies than McKay and Fujinaga's. The best root genre accuracies achieved using MIDI, audio, and combination of them are 0.75, 0.86, and 0.93, respectively, compared to 0.98 of McKay and Fujinaga. Successful classifier combination requires diversity of the base classifiers. We achieve diversity through using certain number of seconds of the MIDI file, different sample rates and sizes for the audio file, and different classification algorithms.
Lippens S, Martens JP, De Mulder T: A comparison of human and automatic musical genre classification. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '04), May 2004, Montreal, Quebec, Canada 4: 233–236.
Basili R, Serafini A, Stellato A: Classification of musical genre: a machine learning approach. Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR '04), October 2004, Barcelona, Spain
Jarvinen T, Toiviainen P, Louhivuori J: Classification and categorization of musical styles with statistical analysis and self-organizing maps. Proceedings of the AISB Symposium on Musical Creativity, April 1999, Edinburgh, Scotland 54–57.
McKay C, Fujinaga I: Automatic genre classification using large high-level musical feature sets. Proceedings of 5th International Conference on Music Information Retrieval (ISMIR '04), October 2004, Barcelona, Spain
Tzanetakis G, Ermolinskyi A, Cook P: Pitch histograms in audio and symbolic music information retrieval. Journal of New Music Research 2003,32(2):143-152. 10.1076/jnmr.188.8.131.5243
Cilibrasi R, Vitányi PMB, de Wolf R: Algorithmic clustering of music based on string compression. Computer Music Journal 2004,28(4):49-67. 10.1162/0148926042728449
Li M, Chen X, Li X, Ma B, Vitányi PMB: The similarity metric. IEEE Transactions on Information Theory 2004,50(12):3250-3264. 10.1109/TIT.2004.838101
Keogh E, Lonardi S, Rtanamahatana CA: Towards parameter-free data mining. Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '04), August 2004, Seattle, Wash, USA 206–215.
Pan D: A tutorial on MPEG/audio compression. IEEE Multimedia 1995,2(2):60-74. 10.1109/93.388209
Aucouturier JJ, Pachet F: Representing musical genre: a state of the art. Journal of New Music Research 2003,32(1):83-93. 10.1076/jnmr.184.108.40.20601
Lidy T, Rauber A: Evaluation of feature extractors and psycho-acoustic transformations for music genre classification. Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR '05), September 2005, London, UK
Tzanetakis G, Cook P: Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing 2002,10(5):293-302. 10.1109/TSA.2002.800560
Xu C, Maddage NC, Shao X, Cao F, Tian Q: Musical genre classification using support vector machines. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '03), April 2003, Hong Kong 5: 429–432.
Gouyon F, Dixon S, Pampalk E, Widmer G: Evaluating rhythmic descriptors for musical genre classification. Proceedings of the 25th International AES Conference, June 2004, London, UK
West K, Cox S: Features and classifiers for the automatic classification of musical audio signals. Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR '04), October 2004, Barcelona, Spain
Sonmez A: Music genre and composer identification by using Kolmogorov distance, M. Sc. thesis. Computer Engineering Department, Istanbul Technical University, Istanbul, Turkey, 2005.
Cataltepe Z, Sonmez A, Adali E: Music classification using Kolmogorov distance. Representation in Music/Musical Representation Congress, October 2005, Istanbul, Turkey
Li T, Ogihara M, Li Q: A comparative study on content-based music genre classification. Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '03), July-August 2003, Toronto, Ontario, Canada 282–289.
Turnbull D, Elkan C: Fast recognition of musical genres using RBF networks. IEEE Transactions on Knowledge and Data Engineering 2005,17(4):580-584.
Duda RO, Hart PE, Stork DG: Pattern Classification. John Wiley & Sons, New York, NY, USA; 2000.
Bergstra J, Casagrande N, Eck D: Genre classification: timbre and rhythm-based multiresolution audio classification. Proceedings of 1st Annual Music Information Retrieval Evaluation eXchange (MIREX) Genre Classification Contest, September 2005, London, UK
Li T, Tzanetakis G: Factors in automatic musical genre classification of audio signals. Proceedings of IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA '03), October 2003, New Paltz, NY, USA
Uitdenbogerd L, Zobel J: Music ranking techniques evaluated. Australian Computer Science Communications 2002,24(1):275-283.
Kuncheva LI: Combining Pattern Classifiers. John Wiley & Sons, New York, NY, USA; 2004.
About this article
Cite this article
Cataltepe, Z., Yaslan, Y. & Sonmez, A. Music Genre Classification Using MIDI and Audio Features. EURASIP J. Adv. Signal Process. 2007, 036409 (2007). https://doi.org/10.1155/2007/36409
- Sample Rate
- Quantum Information
- Classification Algorithm
- Base Classifier
- Audio File