Automatic chord recognition from audio using enhanced pitch class profile

International Computer Music Conference, ICMC 2006

Volumn , Issue , 2006, Pages 306-313

  Lee, Kyogu ^a  

^a STANFORD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTERS;

CHORD RECOGNITION; FEATURE VECTORS; FRAME RATE; MAXIMUM CORRELATIONS; PRODUCT SPECTRUMS;

COMPUTER MUSIC;

EID: 84923550097     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

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2. Brown, J. C. (1990). Calculation of a constant q spectral transform. Journal of the Acoustical Society of America 89(1), 425-434.
3. Fujishima, T. (1999). Real time chord recognition of musical sound: A system using Common Lisp Music. In Proceedings of the International Computer Music Conference, Beijing. International Computer Music Association.
4. Gomez, E. and P. Herrera (2004). Automatic extraction of tonal metadata from polyphonic audio recordings. In Proceedings of the Audio Engineering Society, London. Audio Engineering Society.
5. Harte, C. A. and M. B. Sandler (2005). Automatic chord identification using a quantised chroma gram. In Proceedings of the Audio Engineering Society, Spain. Audio Engineering Society.
6. Krumhansl, C. L. (1990). Cognitive Foundations of Musical Pitch. Oxford University Press.
7. Noll, A. M. (1969). Pitch determination of human speech by the harmonic product spectrum, the harmonic sum spectrum, and a maximum likelihood estimate. In Proceedings of the Symposium on Computer Processing ing Communications, New York, pp. 779-797.
8. Pauws, S. (2004).Musical key extraction from audio. In Proceedings of the International Symposium on Music Information Retrieval, Barcelona, Spain.
9. Schroeder, M. R. (1968). Period histogram and product spectrum: New methods for fundamental-frequency measurement. Journal of the Acoustical Society of America 43(4), 829-834.
10. Sheh, A. and D. P. Ellis (2003). Chord segmentation and recognition using em-trained hidden Markov models. In Proceedings of the International Symposium on Music Information Retrieval, Baltimore, MD.