Efficient and Robust Music Identification With Weighted Finite-State Transducers

IEEE Transactions on Audio, Speech and Language Processing

Volumn 18, Issue 1, 2010, Pages 197-207

  Mohri, Mehryar ^a,b   Weinstein, Eugene ^a,b   Moreno, Pedro J ^b  

^a NEW YORK UNIVERSITY   (United States)

^b GOOGLE INC   (United States)

Author keywords

Content based information retrieval; factor automata; finite state transducers; music identification; suffix automata

Indexed keywords

EID: 85008560093     PISSN: 15587916     EISSN: 15587924     Source Type: Journal    
DOI: 10.1109/TASL.2009.2023170     Document Type: Article

References (25)

1. J. Haitsma, T. Kalker, and J. Oostveen, “Robust audio hashing for content identification,” in Proc. Content-Based Multimedia Indexing (CBMI), Brescia, Italy, Sep. 2001.
2. Y. Ke, D. Hoiem, and R. Sukthankar, “Computer vision for music identification,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognition (CVPR), San Diego, CA, Jun. 2005, pp. 597–604.
3. E. Batlle, J. Masip, and E. Guaus, “Automatic song identification in noisy broadcast audio,” in Proc. IASTED Int. Conf. Signal Image Process., Kauai, HI, 2002.
4. A. L. Wang, “An industrial-strength audio search algorithm,” in Proc. Int. Conf. Music Inf. Retrieval (ISMIR), Baltimore, MD, Oct. 2003, pp. 7–13.
5. M. Covell and S. Baluja, “Waveprint: Efficient wavelet-based audio fingerprinting,” Pattern Recognition, vol. 41, pp. 3461–3466, Nov. 2008.
6. M. Casey, C. Rhodes, and M. Slaney, “Analysis of minimum distances in high-dimensional musical spaces,” IEEE Trans. Audio, Speech, Lang. Process., vol. 16, no. 5, pp. 1015–1028, Jul. 2008.
7. P. Cano, E. Batlle, T. Kalker, and J. Haitsma, “A review of audio fingerprinting,” J. VLSI Signal Process. Syst., vol. 41, pp. 271–284, 2005.
8. M. Mohri, F. C. N. Pereira, and M. Riley, “Weighted finite-state transducers in speech recognition,” Comput. Speech Lang., vol. 16, no. 1, pp. 69–88, 2002.
9. D. Pye, “Content-based methods for the management of digital music,” in Proc. ICASSP, Istanbul, Turkey, Jun. 2000, pp. 2437–2440.
10. B. Logan and A. Salomon, “A music similarity function based on signal analysis,” in IEEE Int. Conf. Multimedia Expo (ICME), Tokyo, Japan, Aug. 2001, pp. 745–748.
11. M. Bacchiani and M. Ostendorf “Joint lexicon, acoustic unit inventory and model design,” Speech Commun., vol. 29, pp. 99–114, Nov. 1999.
12. M. Mohri “Finite-state transducers in language and speech processing,” Comput. Linguist., vol. 23, no. 2, pp. 269–311, 1997.
13. M. Mohri, “Statistical natural language processing,” in Applied Combinatorics on Words, M. Lothaire, Ed. Cambridge, U.K.: Cambridge Univ. Press, 2005.
14. C. Allauzen, M. Mohri, and M. Saraclar, “General indexation of weighted automata–Application to spoken utterance retrieval,” in Proc. Human Lang. Technol. Conf. North Amer. Chapter Assoc. Comput. Linguist. (HLT/NAACL 2004), Workshop Interdisciplinary Approaches to Speech Indexing and Retrieval, Boston, MA, May 2004, pp. 33–40.
15. M. Mohri, P. Moreno, and E. Weinstein, “Factor automata of automata and applications,” in Proc. Int. Conf. Implementation Applicat. Automata (CIAA), Prague, Czech Republic, Jul. 2007.
16. M. Mohri, P. Moreno, and E. Weinstein, “General suffix automaton construction algorithm and space bounds,” Theoretical Comput. Sci., vol. 410, no. 37, Sep. 2009.
17. D. Revuz “Minimisation of acyclic deterministic automata in linear time,” Theoretical Comput. Sci., vol. 92, pp. 181–189, 1992.
18. M. Crochemore “Transducers and repetitions,” Theoretical Comput. Sci., vol. 45, no. 1, pp. 63–86, 1986.
19. A. Blumer, J. Blumer, D. Haussler, A. Ehrenfeucht, M. Chen, and J. Seiferas “The smallest automaton recognizing the subwords of a text,” Theoretical Comput. Sci., vol. 40, pp. 31–55, 1985.
20. C. Alberti, M. Bacchiani, A. Bezman, C. Chelba, A. Drofa, H. Liao, P. Moreno, T. Power, A. Sahuguet, M. Shugrina, and O. Siohan, “An audio indexing system for election video material,” in Proc. ICASSP, Taipei, Taiwan, 2009, pp. 4873–4876.
21. C. Allauzen, M. Riley, J. Schalkwyk, W. Skut, and M. Mohri, “OpenFst: A general and efficient weighted finite-state transducer library,” in Proc. 12th Int. Conf. Implementation Applicat. Automata (CIAA), Prague, Czech Republic, Jul. 2007, pp. 11–23.
22. A. Park and T. Hazen, “ASR dependent techniques for speaker identification,” in Proc. Int. Conf. Spoken Lang. Process. (ICSLP), Denver, CO, Sep. 2002.
23. C. Cortes and V. Vapnik “Support-vector networks,” Mach. Learn., vol. 20, no. 3, pp. 273–297, 1995.
24. V. Vapnik, Statistical Learning Theory. New York: Wiley, 1998.
25. C.-C. Chang and C.-J. Lin, “LIBSVM: A Library for support vector machines,” 2001 [Online]. Available: http://www.csie.ntu.edu.tw/~cjlin/libsvm