LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Silla Jr, Carlos N.; Koerich, Alessandro L.; Kaestner, Celso A.A. (2008)
Publisher: Brazilian Computer Society (SBC)
Languages: English
Types: Article
Subjects: pattern classification, music genre classification, QA76, machine learning, feature selection

Classified by OpenAIRE into

arxiv: Computer Science::Sound
ACM Ref: ComputingMethodologies_PATTERNRECOGNITION
This paper presents a non-conventional approach for the automatic music genre classification problem. The proposed approach uses multiple feature vectors and a pattern recognition ensemble approach, according to space and time decomposition schemes. Despite being music genre classification a multi-class problem, we accomplish the task using a set of binary classifiers, whose results are merged in order to produce the final music genre label (space decomposition). Music segments are also decomposed according to time segments obtained from the beginning, middle and end parts of the original music signal (time-decomposition). The final classification is obtained from the set of individual results, according to a combination procedure. Classical machine learning algorithms such as Naïve-Bayes, Decision Trees, k Nearest-Neighbors, Support Vector Machines and Multi- Layer Perceptron Neural Nets are employed. Experiments were carried out on a novel dataset called Latin Music Database, which contains 3,160 music pieces categorized in 10 musical genres. Experimental results show that the proposed ensemble approach produces better results than the ones obtained from global and individual segment classifiers in most cases. Some experiments related to feature selection were also conducted, using the genetic algorithm paradigm. They show that the most important features for the classification task vary according to their origin in the music signal.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] J.J. Aucouturier; F. Pachet. Representing musical genre: a state of the art. Journal of New Music Research, 32(1):83-93, 2003.
    • [2] J. Bergstra; N. Casagrande; D. Erhan; D. Eck; B. Kégl. Aggregate features and ADABOOST for music classification. Machine Learning, 65(2-3):473- 484, 2006.
    • [3] A. Blum; P. Langley. Selection of relevant features and examples in Machine Learning. Artificial Intelligence, 97(1-2):245-271, 1997.
    • [4] C.H.L. Costa; J. D. Valle Jr; A.L. Koerich. Automatic classification of audio data. IEEE International Conference on Systems, Man, and Cybernetics, pages 562-567, 2004.
    • [5] A.J.D. Craft; G.A. Wiggins; T. Crawford. How many beans make five? the consensus problem in Music Genre Classification and a new evaluation method for single genre categorization systems. Proceedings of the 8th International Conference on Music Information Retrieval, Vienna, Austria, pages 73-76, 2007.
    • [6] M. Dash; H. Liu. Feature selection for classification. Intelligent Data Analysis, 1(1-4):131-156, 1997.
    • [7] T.G. Dietterich. Ensemble methods in Machine Learning. Proceedings of the 1st. International Workshop on Multiple Classifier System, Lecture Notes in Computer Science, 1857:1-15, 2000.
    • [8] J.S. Downie; S.J. Cunningham. Toward a theory of music information retrieval queries: system design implications. Proceedings of the 3rd International Conference on Music Information Retrieval, pages 299-300, 2002.
    • [9] J.S. Downie. The Music Information Retrieval Evaluation eXchange (MIREX). D-Lib Magazine, 12(12), 2006.
    • [10] R. Fiebrink; I. Fujinaga. Feature selection pitfalls and music classification. Proceedings of the 7th International Conference on Music Information Retrieval, Victoria, Canada, pages 340-341, 2006.
    • [11] Y. Freund; R. E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1):119-139, 1997.
    • [12] J. Fürnkranz. Pairwise Classification as an ensemble technique. Proceedings of the 13th European Conference on Machine Learning, Helsinki, Finland, pages 97-110, 2002.
    • [13] M. Grimaldi; P. Cunningham; A. Kokaram. A wavelet packet representation of audio signals for music genre classification using different ensemble and feature selection techniques. Proceedings of the 5th ACM SIGMM International Workshop on Multimedia Information Retrieval, ACM Press, pages 102-108, 2003.
    • [14] M. Grimaldi; P. Cunningham; A. Kokaram. An evaluation of alternative feature selection strategies and ensemble techniques for classifying music. Workshop on Multimedia Discovery and Mining, 14th European Conference on Machine Learning, 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, Dubrovnik, Croatia, 2003.
    • [15] S. Hacker. MP3: The Definitive Guide. O'Reilly Publishers, 2000.
    • [16] T.K. Ho. Nearest neighbors in random subspaces. Advances in Pattern Recognition, Joint IAPR International Workshops SSPR and SPR, Lecture Notes in Computer Science, 1451:640-648, 1998.
    • [17] J. Kittler; M. Hatef; R.P.W. Duin; and J. Matas. On Combining Classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(3):226- 239, 1998.
    • [18] A.L. Koerich; C. Poitevin. Combination of homogenuous classifiers for musical genre classification. IEEE International Conference on Systems, Man and Cybernetics, IEEE Press, Hawaii, USA, pages 554-559, 2005.
    • [19] K. Kosina. Music Genre Recognition. MSc. Dissertation, Fachschule Hagenberg, June 2002.
    • [20] J.H. Lee; J.S. Downie. Survey of music information needs, uses, and seeking behaviours preliminary findings. Proceedings of the 5th International Conference on Music Information Retrieval, Barcelona, Spain, pages 441-446, 2004.
    • [21] T. Li; M. Ogihara; Q. Li. A Comparative study on content-based Music Genre Classification. Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval, Toronto, ACM Press, pages 282-289, 2003.
    • [22] M. Li; R. Sleep. Genre classification via an LZ78- based string kernel. Proceedings of the 6th International Conference on Music Information Retrieval, London, United Kingdom, pages 252-259, 2005.
    • [23] T. Li; M. Ogihara. Music Genre Classification with taxonomy. Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, Philadelphia, USA, pages 197-200, 2005.
    • [24] H. Liu; L. Yu. Feature extraction, selection, and construction. The Handbook of Data Mining, Lawrence Erlbaum Publishers, chapter 16, pages 409-424, 2003.
    • [25] D. McEnnis; C. McKay; I. Fujinaga. Overview of OMEN (On-demand Metadata Extraction Network). Proceedings of the International Conference on Music Information Retrieval, Victoria, Canada, pages 7-12, 2006.
    • [26] D. McEnnis; S.J. Cunningham. Sociology and music recommendation systems. Proceedings of the 8th International Conference on Music Information Retrieval, Vienna, Austria, pages 185-186, 2007.
    • [27] A. Meng; P. Ahrendt; J. Larsen. Improving Music Genre Classification by short-time feature integration. Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Philadelphia, USA, pages 497-500, 2005.
    • [28] T. M. Mitchell. Machine Learning. McGraw-Hill, 1997.
    • [29] L.C. Molina; L. Belanche; A. Nebot. Feature selection algorithms: a survey and experimental evaluation. Proceedings of the IEEE International Conference on Data Mining, Maebashi City, Japan, pages 306-313, 2002.
    • [30] E. Pampalk; A. Rauber; D. Merkl. Content-Based organization and visualization of music archives. Proceedings of ACM Multimedia, Juan-les-Pins, France, pages 570-579, 2002.
    • [31] C.N. Silla Jr.; C.A.A. Kaestner; A. L. Koerich. Time-Space ensemble strategies for automatic music genre classification. Proceedings of the 18th Brazilian Symposium on Artificial Intelligence, Ribeirão Preto, Brazil, Lecture Notes in Computer Science, 4140:339-348, 2006.
    • [32] C.N. Silla Jr.; C.A.A. Kaestner; A. L. Koerich. The Latin Music Database: a database for the automatic classification of music genres (in portuguese). Proceedings of 11th Brazilian Symposium on Computer Music, São Paulo, BR, pages 167-174, 2007.
    • [33] C.N. Silla Jr.; C.A.A. Kaestner; A.L. Koerich. Automatic music genre classification using ensemble of classifiers. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, Montreal, Canada, pages 1687-1692, 2007.
    • [34] C. N. Silla Jr.. Classifiers Combination for Automatic Music Classification (in portuguese). MSc. Dissertation, Graduate Program in Applied Computer Science, Pontifical Catholic University of Paraná, January 2007.
    • [35] G. Tzanetakis; P. Cook. Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing, 10(5):293-302, 2002.
    • [36] F. Vignoli. Digital music interaction concepts: a user study. Proceedings of the 5th International Conference on Music Information Retrieval, Barcelona, Spain, pages 415-420, 2004.
    • [37] I. H. Witten; E. Frank. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, 2005.
    • [38] Y. Yaslan; Z. Cataltepe. Audio music genre classification using different classifiers and feature selection methods. Proceedings of the International Conference on Pattern Recognition, Hong-Kong, China, pages 573-576, 2006.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article