Towards a Complete Classical Music Companion

We present a system that listens to music on-line and almost instantly identifies the piece the performers are playing and the exact position in the musical score. This is achieved via a combination of a state-of-the-art audio-to-note transcription algorithm and a novel symbolic fingerprinting method. The speed and precision of the system are evaluated in systematic experiments with a large corpus of classical music recordings. The results indicate extremely fast and accurate recognition performance — a level of performance, in fact, that even human experts in classical music will find hard to match.

[1]  Gerhard Widmer,et al.  The Magaloff Project: An Interim Report , 2010 .

[2]  Gerhard Widmer,et al.  Discovering simple rules in complex data: A meta-learning algorithm and some surprising musical discoveries , 2003, Artif. Intell..

[3]  Avery Wang,et al.  An Industrial Strength Audio Search Algorithm , 2003, ISMIR.

[4]  Gerhard Widmer,et al.  Towards Effective 'Any-Time' Music Tracking , 2010, STAIRS.

[5]  Gerhard Widmer,et al.  Automatic Page Turning for Musicians via Real-Time Machine Listening , 2008, ECAI.

[6]  Meinard Müller,et al.  Efficient Index-Based Audio Matching , 2008, IEEE Transactions on Audio, Speech, and Language Processing.

[7]  Markus Schedl,et al.  Polyphonic piano note transcription with recurrent neural networks , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[8]  Ton Kalker,et al.  A Highly Robust Audio Fingerprinting System , 2002, ISMIR.

[9]  Roland Badeau,et al.  Multipitch Estimation of Piano Sounds Using a New Probabilistic Spectral Smoothness Principle , 2010, IEEE Transactions on Audio, Speech, and Language Processing.