Digital Signal Processing Reference
In-Depth Information
50. Sethares, W.A.: Local consonance and the relationship between timbre and scale. J. Acoust.
Soc. Am.
94
(3), 1218-1228 (1993)
51. Gouyon, F., Klapuri, A.P., Dixon, S., Alonso, M., Tzanetakis, G., Uhle, C., Cano, P.: An
experimental comparison of audio tempo induction algorithms. IEEE Trans. Audio, Speech,
Lang. Process.
14
(5), 1832-1844 (2006)
52. Dixon, S.: Onset detection revisited. In: Proceedings of DAFx-06, pp. 133-137, Montreal,
Canada (2006)
53. Zhou, R., Reiss, J.: Music onset detection combining energy-based and pitch-based
approaches. In: Proceedings of MIREX as part of the 8th International Conference on Music
Information Retrieval (ISMIR). Sept 23-27. P. 4, Vienna, Austria (2007)
54. Röbel, A.: Onset detection by means of transient peak classification in harmonic bands. In:
Proceedings of MIREX as part of the 10th International Conference on Music Information
Retrieval (ISMIR), P. 2, Kobe, Japan (2009)
55. Klapuri, A.: Sound onset detection by applying psychoacoustic knowledge. In Proceedings
of ICASSP, vol. 6, pp. 3089-3092 (1999)
56. Bello, J., Daudet, L., Abdallah, S., Duxbury, C., Davies, M., Sandler, M.: A tutorial on onset
detection in music signals. IEEE Trans. Speech Audio Process.
13
(5), 1035-1047 (2005)
57. Duxbury, C., Bello, J.P., Davies, M.: M. Sandler. Complex domain onset detection for musical
signals. In: Proceedings of Digital Audio Effects Workshop (DAFx-03) pp. 1-4, London, UK
(2003)
58. Collins, N.: Using a pitch detector for onset detection. In Proceedings of ISMIR, pp. 100-106
(2005)
59. Basseville, M., Nikiforov, I.V.: Detection of Abrupt Changes: Theory and Application.
Prentice-Hall, Englewood Cliffs (1993)
60. Lacoste, A., Eck, D.: Onset detection with artificial neural networks. In: Proceedings of
MIREX as part of the 6th International Conference on Music Information Retrieval (ISMIR),
P. 4, London, UK (2005)
61. Graves, A.: Supervised sequence labelling with recurrent neural networks. Ph.D. Thesis,
Technische Universität München (2008)
62. Collins, N.: A comparison of sound onset detection algorithms with emphasis on psychoa-
coustically motivated detection functions. In: Proceedings of AES Convention 118, pp. 28-31
(2005)
63. Handel, S.: Listening: An Introduction to the Perception of Auditory Events. MIT Press,
Cambridge (1989)
64. Böck, S.: Onset Detector 2011. In: Proceedings Annual Meeting of the MIREX 2011 Com-
munity as Part of the 12th International Conference on Music Information Retrieval. p. 2.
ISMIR, ISMIR (2011)
65. Böck, S., Arzt, A., Krebs, F., Schedl, M.: Online real-time onset detection with recurrent
neural networks. In Proceedings of the 15th International Conference on Digital Audio Effects
(DAFx-12), p. 4. New York, UK (2012)
66. Klapuri, A.P.: Musical meter estimation and music transcription. In: Proceedings of Cam-
bridge Music Processing Colloquium, Cambridge University, UK (2003)
67. Gouyon, F., Herrera, P.: Determination of the meter of musical audio signals: seeking recur-
rences in beat segment descriptors. In: AES 114th Convention, Amsterdam, The Netherlands
(2003)
68. Gouyon, F., Dixon, S., Pampalk, E., Widmer, G.: Evaluating rhythmic descriptors for musical
genre classification. In: Proceedings of the AES 25th International Conference, pp. 196-204.
London, UK (2004)
69. Grosche, P., Müller, M., Kurth, F.: Cyclic tempogram—a mid-level tempo representation for
music signals. In: Proceedings of ICASSP, pp. 5522-5525. Dallas, TX (2010)
70. Kirovski, D., Attias, H.: Beat-ID: identifying music with beat analysis. In: Proceedings of the
International Workshop on Multimedia Signal Processing, IEEE, pp. 190-193, St. Thomas,
US Virgin Islands (2002)
