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On Limiting Donor Usage for Imputation of Missing Data via Hot Deck Methods Read chapter Read first chapter

2014 | OriginalPaper | Chapter

Tone Onset Detection Using an Auditory Model

Authors : Nadja Bauer, Klaus Friedrichs, Dominik Kirchhoff, Julia Schiffner, Claus Weihs

Published in: Data Analysis, Machine Learning and Knowledge Discovery

Publisher: Springer International Publishing

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Abstract

Onset detection is an important step for music transcription and other tasks frequently encountered in music processing. Although several approaches have been developed for this task, neither of them works well under all circumstances. In Bauer et al. (Einfluss der Musikinstrumente auf die Güte der Einsatzzeiterkennung, 2012) we investigated the influence of several factors like instrumentation on the accuracy of onset detection. In this work, this investigation is extended by a computational model of the human auditory periphery. Instead of the original signal the output of the simulated auditory nerve fibers is used. The main challenge here is combining the outputs of all auditory nerve fibers to one feature for onset detection. Different approaches are presented and compared. Our investigation shows that using the auditory model output leads to essential improvements of the onset detection rate for some instruments compared to previous results.

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Footnotes
1
v[i] is the notation for the i-th element of vector v.
 
2
BPM: beats per minute.
 
Literature
Bauer, N., Schiffner, J., & Weihs, C. (2012). Einfluss der Musikinstrumente auf die Güte der Einsatzzeiterkennung. Discussion Paper 10/2012. SFB 823, TU Dortmund.
Bello, J. P., Daudet, L., Abdallah, S., Duxbury, C., Davies, M., & Sandler, M. B. (2005). A tutorial on onset detection in music signals. IEEE Transactions on Speech and Audio Processing, 13(5), 1035–1047.CrossRef
Benetos, E., Holzapfel, A., & Stylianou Y. (2009). Pitched instrument onset detection based on auditory spectra. In 10th International Society for Music Information Retrieval Conference (ISMIR 2009) Kobe, Japan (pp. 105–110).
Böck, S., Krebs, F., & Schedl, M. (2012). Evaluating the online capabilities of onset detection methods. In Proceedings of the 13th International Conference on Music Information Retrieval (ISMIR 2012) Porto, Portugal (pp. 49–54).
Dixon, S. (2006). Onset detection revisited. In Proceedings of the 9th International Conference on Digital Audio Effects (DAFx-06) Montreal, Canada (pp. 133–137).
Goto, M., Hashiguchi, H., Nishimura, T., & Oka, R. (2003) RWC music database: Music genre database and musical instrument sound database. In Proceedings of the 4th International Conference on Music Information Retrieval (ISMIR 2003) Baltimore, USA (pp. 229–230).
Krasser, J., Abeßer, J., & Großmann, H. (2012). Improved music similarity computation based on tone objects. In Proceedings of the 7th Audio Mostly Conference Corfu, Greece (pp. 47–54).
Meddis, R. (2006). Auditory-nerve first-spike latency and auditory absolute threshold: A computer model. Journal of the Acoustical Society of America, 116, 406–417.CrossRef
Rosão, C., Ribeiro, R., & Martins de matoset, D. (2012). Influence of peak selection methods on onset detection. In Proceedings of the 13th International Conference on Music Information Retrieval (ISMIR 2012) Porto, Portugal (pp. 517–522).
Metadata
Title
Tone Onset Detection Using an Auditory Model
Authors
Nadja Bauer
Klaus Friedrichs
Dominik Kirchhoff
Julia Schiffner
Claus Weihs
Copyright Year
2014
Book
Data Analysis, Machine Learning and Knowledge Discovery

Print ISBN: 978-3-319-01594-1

Electronic ISBN: 978-3-319-01595-8

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-3-319-01595-8_34

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