¿El caballo viejo? Latin genre recognition with deep learning and spectral periodicity

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 9110, Issue , 2015, Pages 335-346

  Sturm, Bob L ^a   Kereliuk, Corey ^b   Larsen, Jan ^b  

^a QUEEN MARY UNIVERSITY OF LONDON   (United Kingdom)

^b TECHNICAL UNIVERSITY OF DENMARK   (Denmark)

Author keywords

Deep learning; Evaluation; Genre; Machinemusic listening

Indexed keywords

INSECTICIDES;

DEEP LEARNING; DEEP NEURAL NETWORKS; EVALUATION; GENRE; GENRE CLASSIFICATION; INPUT FEATURES; MACHINEMUSIC LISTENING; TIME CHARACTERISTICS;

STATISTICAL TESTS;

EID: 84948958347     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-20603-5_34     Document Type: Conference Paper

References (19)

1. Aucouturier, J.J., Pachet, F.: Representing music genre: A state of the art. J. New Music Res. 32(1), 83–93 (2003)
2. Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H.: Greedy layer-wise training of deep networks. Adv. Neural Inf. Process. Syst. 19, 153 (2007)
3. Deng, L., Yu, D.: Deep Learning: Methods and Applications. Now Publishers, Hanover (2014)
4. Dixon, S., Gouyon, F., Widmer, G.: Towards characterisation of music via rhythmic patterns. In: Proceedings of the ISMIR, pp. 509–517 (2004)
5. Esparza, T., Bello, J., Humphrey, E.: From genre classification to rhythm similarity: Computational and musicological insights. J. New Music Res. 44, 39–57 (2014)
6. Frow, J.: Genre. Routledge, New York (2005)
7. Gouyon, F., Dixon, S., Pampalk, E., Widmer, G.: Evaluating rhythmic descriptors for musical genre classification. In: Proceedings of the Audio Engineering Society Conference, pp. 196–204 (2004)
8. Hinton, G., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)
9. Humphrey, E., Bello, J., LeCun, Y.: Feature learning and deep architectures: New directions for music informatics. J. Intell. Info. Syst. 41(3), 461–481 (2013)
10. Pfungst, O.: Clever Hans (The horse of Mr. Von Osten): A Contribution to Experimental Animal and Human Psychology. Henry Holt, New York (1911)
11. Pikrakis, A.: A deep learning approach to rhythm modeling with applications. In: Proceedings of International Workshop Machine Learning and Music (2013)
12. Saxe, A.M., McClelland, J.L., Ganguli, S.: Exact solutions to the nonlinear dynamics of learning in deep linear neural networks. CoRR abs/1312.6120 (2013)
13. Silla, C.N., Koerich, A.L., Kaestner, C.A.A.: The Latin music database. In: Proceedings of ISMIR (2008)
14. Slaney, M.: Auditory toolbox. Technical report, Interval Research Corporation (1998)
15. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
16. Sturm, B.L.: Classification accuracy is not enough: On the evaluation of music genre recognition systems. J. Intell. Info. Syst. 41(3), 371–406 (2013)
17. Sturm, B.L.: A simple method to determine if a music information retrieval system is a “horse”. IEEE Trans. Multimedia 16(6), 1636–1644 (2014)
18. Sturm, B.L.: The state of the art ten years after a state of the art: Future research in music information retrieval. J. New Music Res. 43(2), 147–172 (2014)
19. Sturm, B.L., Kereliuk, C., Pikrakis, A.: A closer look at deep learning neural networks with low-level spectral periodicity features. In: Proceedings of the International Workshop on Cognitive Information Processing (2014)