References
- Alonso, M., Richard, G. and David, B. (2005). Extracting note onsets from musical recordings, Proceedings of the IEEE International Conference on Multimedia and Expo 2005, Amsterdam, The Netherlands, pp. 1–4.
- Bartkowiak, M. and Januszkiewicz, Ł. (2012). Hybrid sinusoidal modeling of music with near transparent audio quality, Proceedings of the Joint AES/IEEE Conference NTAV-SPA, Łódź, Poland, pp. 91–96.
- Bello, J., Daudet, L., Abdullah, S., Duxbury, C., Davies, M. and Sandler, M. (2005). A tutorial on onset detection in music signals, IEEE Transactions on Speech and Audio Processing13(5): 1035–1047.
- Bello, P. and Sandler, M. (2003). Phase-based note onset detection for music signals, Proceedings of the IEEE Conference on Acoustics, Speech, and Signal Processing ICASSP, Hong Kong, Vol. 5, pp. 441–444.
- Bishop, C.M. (1995). Neural Networks for Pattern Recognition, Oxford University Press, New York, NY.
- Böck, S., Arzt, A., Krebs, F. and Schedl, M. (2012). Online real-time onset detection with recurrent neural networks, Proceedings of the 15th International Conference on Digital Audio Effects (DAFx 2012), York, UK, pp. 1–4.
- Collins, N. (2005). A comparison of sound onset detection algorithms with emphasis on psychoacoustically motivated detection functions, Proceedings of the AES 118th International Convention, Barcelona, Spain, pp. 28–31.
- Daudet, L., Richard, G. and Leveau, P. (2004). Methodology and tools for the evaluation of automatic onset detection algorithms in music, 5th International Conference on Music Information Retrieval, ISMIR 2004, Barcelona, Spain, pp. 72–75.
- Davy, M. and Godsill, S.J. (2002). Detection of abrupt spectral changes using support vector machines: An application to audio signal segmentation, IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2002, Orlando, FL, USA, pp. 1313–1316.
- Dixon, S. (2006). Onset detection revisited, Proceedings of the International Conference on Digital Audio Effects (DAFx-06), Montreal, Quebec, Canada, pp. 133–137.
- Duxbury, C., Bello, J., Davies, M. and Sandler, M. (2003). Complex domain onset detection for musical signals, Proceedings of the 6th International Conference on Digital Audio Effects (DAFx-03), London, UK, pp. 1–4.
- Eyben, F., Böck, S., Schuller, B. and Graves, A. (2010). Universal onset detection with bidirectional long shortterm memory, Neural Networks, 11 th International Society for Music Information Retrieval Conference (ISMIR 2010), Utrecht, The Netherlands, pp. 589–594.
- Huang, S., Wang, L., Hu, S., Jiang, H. and Xu, B. (2008). Query by humming via multiscale transportation distance in random query occurrence context, IEEE International Conference on Multimedia and Expo, ICME 2008, Hannover, Germany, pp. 1225–1228.
- Lacoste, A. and Eck, D. (2007). A supervised classification algorithm for note onset detection, EURASIP Journal of Advanced Signal Processing2007: 153–153.
- Laroche, J. (2003). Efficient tempo and beat tracking in audio recordings, Journal of the Audio Engineering Society51(4): 226–233.
- Lee, W.-C. and Kuo, C.-C. (2006). Musical onset detection based on adaptive linear prediction, IEEE International Conference on Multimedia and Expo, ICME 2006, Toronto, Ontario, Canada, pp. 957–960.
- Lerch, A. (2012). An Introduction to Audio Content Analysis: Applications in Signal Processing and Music Informatics, Wiley/IEEE Press, Hoboken, NJ.
- MIREX (2013). Audio onset detection results in Music Information Retrieval Evaluation eXchange MIREX, 2013, http://nema.lis.illinois.edu/nema_out/mirex2013/results/aod/summary.html.
- Peeters, G. (2005). Time variable tempo detection and beat marking, Proceedings of the International Computer Music Conference, ICMC 2005, Barcelona, Spain, pp. 1–4.
- Quintela, N.D., Giménez, A.P. and Guijarro, S.T. (2009). A comparison of score-level fusion rules for onset detection in music signals, Proceedings of the 10th International Society for Music Information Retrieval Conference, ISMIR09, Kobe, Japan, pp. 117–121.
- Rabenstein, R. and Petrausch, S. (2008). Block-based physical modeling with applications in musical acoustics, International Journal of Applied Mathematics and Computer Science18(3): 295–305, DOI: 10.2478/v10006-008-0027-6.
- Repp, B.H. (1996). Patterns of note onset asynchronies in expressive piano performance, Journal of the Acoustical Society of America100(6): 3917–3932.
- Schlüter, J. and Böck, S. (2014). Improved musical onset detection with convolutional neural networks, Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2014), Florence, Italy, pp. 6979–6983.
- Stasiak, B. (2015). Results repository, http://ics.p.lodz.pl/~basta/NN_MULTI_ODF_FUSION/Stasiak_OnsetDB.zip.
- Tian, M., Fazekas, G., Black, D.A.A. and Sandler, M. (2014). Design and evaluation of onset detectors using different fusion policies, 15th International Society of Music Information Retrieval (ISMIR) Conference, ISMIR 2014, Taipei, Taiwan, pp. 631–636.
- Typke, R., Wiering, F. and Veltkamp, R.C. (2007). Transportation distances and human perception of melodic similarity, Musicae Scientiae11(1): 153–181.
- Yin, J., Wang, Y. and Hsu, D. (2005). Digital violin tutor: An integrated system for beginning violin learners, in H. Zhang et al. (Eds.), ACM Multimedia, ACM, New York, NY, pp. 976–985.
- Zhang, B. and Wang, Y. (2009). Automatic music transcription using audio-visual fusion for violin practice in home environment, Technical Report TRA7/09, National University of Singapore, Singapore.