Abstract
This paper presents a comparison between traditional and automatic approaches for the extraction of an audio descriptor to recognize chord into classes. The traditional approach requires signal processing (SP) skills, constraining it to be used only by expert users. The Extractor Discovery System (EDS) [1] is a recent approach, which can also be useful for non expert users, since it intends to discover such descriptors automatically. This work compares the results from a classic approach for chord recognition, namely the use of KNN-learners over Pitch Class Profiles (PCP), with the results from EDS when operated by a non SP expert.
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References
Pachet, F., Zils, A.: Automatic Extraction of Music Descriptors from Acoustic Signals. In: Proceedings of Fifth International Conference on Music Information Retrieval (ISMIR 2004), Barcelona (2004)
Zils, A., Pachet, F.: Extracting Automatically the Perceived Intensity of Music Titles. In: Proceedings of the 6th COST-G6 Conference on Digital Audio Effects, DAFX 2003 (2003)
Gómez, E., Herrera, P.: Estimating the tonality of polyphonic audio files: cognitive versus machine learning modelling strategies. In: Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), Barcelona (2004)
Sheh, A., Ellis, D.: Chord Segmentation and Recognition using EM-Trained Hidden Markov Models. In: Proceedings of the 4th International Symposium on Music Information Retrieval (ISMIR 2003), Baltimore, USA (2003)
Yoshioka, T., Kitahara, T., Komatani, K., Ogata, T., Okuno, H.: Automatic chord transcription with concurrent recognition of chord symbols and boundaries. In: Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR 2004), Barcelona (2004)
Fujishima, T.: Real-time chord recognition of musical sound: a system using Common Lisp Music. In: Proceedings of International Computer Music Conference (ICMC 1999), Beijing (1999)
Bartsch, M.A., Wakefield, G.H.: To catch a chorus: Using chromabased representation for audio thumbnailing. In: Proceedings of International. Workshop on Applications of Signal Processing to Audio and Acoustics, Mohonk, USA (2001)
Pardo, B., Birmingham, W.P.: The Chordal Analysis of Tonal Music. The University of Michigan, Department of Electrical Engineering and Computer Science Technical Report CSE-TR-439-01 (2001)
Mitchell, T.: Machine Learning. The McGraw-Hill Companies, Inc., New York (1997)
Cabral, G., Zanforlin, I., Santana, H., Lima, R., Ramalho, G.: D’accord Guitar: An Innovative Guitar Performance System. In: Proceedings of Journées d’Informatique Musicale (JIM 2001), Bourges (2001)
Koza, J.R.: Genetic Programming: on the programming of computers by means of natural selection. The MIT Press, Cambridge, USA
Gómez, E., Herrera, P.: Automatic Extraction of Tonal Metadata from Polyphonic Audio Recordings. In: Proceedings of 25th International AES Conference, London (2004)
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© 2006 Springer-Verlag Berlin Heidelberg
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Cabral, G., Pachet, F., Briot, JP. (2006). Recognizing Chords with EDS: Part One. In: Kronland-Martinet, R., Voinier, T., Ystad, S. (eds) Computer Music Modeling and Retrieval. CMMR 2005. Lecture Notes in Computer Science, vol 3902. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11751069_17
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DOI: https://doi.org/10.1007/11751069_17
Publisher Name: Springer, Berlin, Heidelberg
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