Multiple-F0 Estimation of Piano Sounds Exploiting Spectral Structure and Temporal Evolution

ISCA Workshop on Statistical and Perceptual Audio Processing, SAPA 2010

Volumn , Issue , 2010, Pages

  Benetos, Emmanouil ^a   Dixon, Simon ^a  

^a QUEEN MARY UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

common amplitude modulation; multiple F0 estimation; resonator time frequency image

Indexed keywords

AMPLITUDE MODULATION; FREQUENCY ESTIMATION; MUSICAL INSTRUMENTS;

CANDIDATE SELECTION; COMMON AMPLITUDE MODULATION; MULTIPLE FUNDAMENTAL FREQUENCY ESTIMATIONS; MULTIPLE-F0 ESTIMATIONS; PIANO SOUNDS; RESONATOR TIME-FREQUENCY IMAGE; SPECTRAL STRUCTURE; STRUCTURE EVOLUTION; TEMPORAL EVOLUTION; TIME-FREQUENCY IMAGES;

RESONATORS;

EID: 80053007249     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. J. P. Bello, “Towards the automated analysis of simple polyphonic music: a knowledge-based approach,” PhD Diss., Queen Mary, University of London, Jan. 2003.
2. J. P. Bello, L. Daudet, S. Abdallah, C. Duxbury, M. Davies, and M. Sandler, “A tutorial on onset detection of music signals,” IEEE Trans. Speech and Audio Proc., Vol. 13, no. 5, pp. 1035–1047, Sep. 2005.
3. J. C. Brown, “Calculation of a constant Q spectral transform,” J. Acoustical Society of America, Vol. 89, no. 1, pp. 425–434, Jan. 1991.
4. A. de Cheveigné, “Multiple F0 estimation,” in D. L. Wang and G. J. Brown (Eds), Computational Auditory Scene Analysis, Algorithms and Applications, IEEE Press/Wiley, pp. 45–79, 2006.
5. V. Emiya, R. Badeau, and B. David, “Multipitch estimation of piano sounds using a new probabilistic spectral smoothness principle,” IEEE Trans. Audio, Speech and Language Processing, Vol. 18, no. 6, pp. 1643–1654, Aug. 2010.
6. D. Gunawan and D. Sen, “Identification of partials in polyphonic mixtures based on temporal envelope similarity,” in Proc. AES 123rd Convention, Oct. 2007.
7. I. Guyon, J. Makhoul, R. Schwartz, and V. Vapnik, “What size test set gives good error estimates?,” IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 20, no. 1, pp. 52–64, Jan. 1998.
8. A. Klapuri, “Multiple fundamental frequency estimation based on harmonicity and spectral smoothness,” IEEE Trans. Audio, Speech, and Language Processing, Vol. 11, no. 6, pp. 804–816, Nov. 2003.
9. A. Klapuri and M. Davy (Eds), Signal Processing Methods for Music Transcription, Springer-Verlag, New York, 2006.
10. A. Klapuri, “A method for visualizing the pitch content of polyphonic music signals,” in Proc. 10th Int. Conf. Music Information Retrieval, pp. 615–620, Oct. 2009.
11. Y. Li, J. Woodruff, and D. L. Wang, “Monaural musical sound separation based on pitch and common amplitude modulation,” IEEE Trans. Audio, Speech, and Language Processing, Vol. 17, no. 7, pp. 1361–1371, Sep. 2009.
12. Music Information Retrieval Evaluation eXchange (MIREX), http://music-ir.org/mirexwiki/.
13. L. I. Ortiz-Berenguer, F. J. Casajús-Quirós, M. Torres-Guijarro, and J. A. Beracoechea, “Piano transcription using pattern recognition: aspects on parameter extraction,” in Proc. 7th Int. Conf. Digital Audio Effects, pp. 212–216, Oct. 2004.
14. D. Stowell and M. Plumbley, “Adaptive whitening for improved real-time audio onset detection,” in Proc Int. Computer Music Conf., pp. 312–319, Aug. 2007.
15. E. Vincent, N. Bertin, and R. Badeau, “Harmonic and inharmonic nonnegative matrix factorization for polyphonic pitch transcription,” in Proc. 2008 IEEE Int. Conf. Acoustics, Speech, and Signal Processing, pp. 109–112, Apr. 2008.
16. C. Yeh, “Multiple fundamental frequency estimation of polyphonic recordings,” PhD Diss., École Doctorale Edite, Université Paris VI - Pierre et Marie Curie, Jun. 2008.
17. R. Zhou,”Feature Extraction of Musical Content for Automatic Music Transcription,” PhD Diss., Swiss Federal Institute of Technology, Lausanne, Oct. 2006.