Abstract
Note recognizer is an online web application. In order to overcome the performance issues of the internet infrastructure (browser, devices, OS platforms) traditional algorithms have been re-designed and novel processes based on the Web Audio API have been implemented. It is the first time that open standard web tools offered in all the commercial browsers are used to build an application that usually required dedicated signal processing libraries. These novel processes and algorithms provide MIDI (Musical Instrument Digital Interface) information out of audio files or microphone. Our application may assist musical education by allowing students to transform their inspiration or a performance into notes.
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Bello, J.P., Daudet, L., Abdallah, S., Duxbury, C., Davies, M., Sandler, M.B.: A tutorial on onset detection in music signals. IEEE Trans. Speech Audio Process. 13(5), 1035–1047 (2005)
Rosão, C., Ribeiro, R., Matos, D.M.: Comparing onset detection methods based on spectral. In: Proceedings of the Workshop on Open Source and Design of Communication, pp. 71–78. ACM (2012)
Man, T.K.: Tempo Extraction using the Di. Hong Kong University of Science and Technology, Hong Kong (2006)
Cai, W.: Analysis of Acoustic Feature Extraction. University of Rochester, New York (2013)
Cnx. (n.d.) (2017). http://cnx.org/contents/8b900091-908f-42ad-b93d-806415434b46@2/Pitch-Detection-Algorithms
Bello, J.P., Duxbury, C., Davies, M., Sandler, M.: On the use of phase and energy for musical onset detection in the complex domain. IEEE Signal Process. Lett. 11(6), 553–556 (2004)
Hess, A.: Beat Detection for Automated Music Transcription: An exploration of Onset Detection Algorithms. MSc thesis, Thomas J. Watson School of Engineering and Applied Science State University of New York at Binghamton (2011)
The future of music technology (2017). http://www.yalescientific.org/2012/03/the-future-of-music-technology/. Accessed 2 June 2017
The future of music technology (2017). http://spectrum.ieee.org/tech-talk/consumer-electronics/audiovideo/the-future-of-music-technology. Accessed 2 June 2017
Bayesian Music Transcription: A. Taylan Cemgil Radboud, PHD Thesis, University of Nijmegen, Netherlands (November, 2004) (2004)
Klapuri, A., Davy, M. (eds.): Signal Processing Methods for Music Transcription. Springer, New York (2006). https://doi.org/10.1007/0-387-32845-9
Bello, J.P., Sandler, M.: Phase-based note onset detection for music signals. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-03), Hong Kong, 6–10 April 2003
Beat detection using web audio (2017). http://joesul.li/van/beat-detection-using-web-audio/. Accessed 2 June 2017
Goto, M.: A predominant-f0 estimation method for polyphonic musical audio signals. In: 18th International Congress on Acoustics, pp. 1085–1088 (2004)
Implementation of Pitch Detection (2017). https://github.com/cwilso/pitchdetect. Accessed 2 June 2017
Wikipedia. (n.d.) (2017). https://en.wikipedia.org/wiki/Autocorrelation. Accessed 2 June 2017
Implementation of the Moving Median filter (2017). https://github.com/mikolalysenko/moving-median. Accessed 2 June 2017
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Fragkopoulos, M., Malamos, A.G., Panagiotakis, S. (2018). Note Recognizer: Web Application that Assists Music Learning by Detecting and Processing Musical Characteristics from Audio Files or Microphone in Real-Time. In: Brooks, A., Brooks, E., Vidakis, N. (eds) Interactivity, Game Creation, Design, Learning, and Innovation. ArtsIT DLI 2017 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 229. Springer, Cham. https://doi.org/10.1007/978-3-319-76908-0_39
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