A discriminative model for polyphonic piano transcription
EURASIP Journal on Applied Signal Processing
Event based transcription system for polyphonic piano music
Signal Processing
ICC'08 Proceedings of the 12th WSEAS international conference on Circuits
Signal-to-score music transcription using graphical models
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Transcription of polyphonic piano music by means of memory-based classification method
Proceedings of the 2009 conference on Neural Nets WIRN09: Proceedings of the 19th Italian Workshop on Neural Nets, Vietri sul Mare, Salerno, Italy, May 28--30 2009
A computationally efficient method for polyphonic pitch estimation
EURASIP Journal on Advances in Signal Processing
Note onset detection for the transcription of polyphonic piano music
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Periodic signal modeling for the octave problem in music transcription
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
CSECS'09 Proceedings of the 8th WSEAS International Conference on Circuits, systems, electronics, control & signal processing
Music scene-adaptive harmonic dictionary for unsupervised note-event detection
IEEE Transactions on Audio, Speech, and Language Processing
Generative spectrogram factorization models for polyphonic piano transcription
IEEE Transactions on Audio, Speech, and Language Processing
Adaptive harmonic spectral decomposition for multiple pitch estimation
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Audio, Speech, and Language Processing
Multipitch estimation of piano sounds using a new probabilistic spectral smoothness principle
IEEE Transactions on Audio, Speech, and Language Processing
EURASIP Journal on Advances in Signal Processing - Special issue on digital audio effects
Automatic music transcription based on non-negative matrix factorization
ICS'10 Proceedings of the 14th WSEAS international conference on Systems: part of the 14th WSEAS CSCC multiconference - Volume I
What signal processing can do for the music
CMMR'10 Proceedings of the 7th international conference on Exploring music contents
Musical pitch estimation using a supervised single hidden layer feed-forward neural network
Expert Systems with Applications: An International Journal
Multiple fundamental frequency estimation based on sparse representations in a structured dictionary
Digital Signal Processing
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In this paper, we present a connectionist approach to automatic transcription of polyphonic piano music. We first compare the performance of several neural network models on the task of recognizing tones from time-frequency representation of a musical signal. We then propose a new partial tracking technique, based on a combination of an auditory model and adaptive oscillator networks. We show how synchronization of adaptive oscillators can be exploited to track partials in a musical signal. We also present an extension of our technique for tracking individual partials to a method for tracking groups of partials by joining adaptive oscillators into networks. We show that oscillator networks improve the accuracy of transcription with neural networks. We also provide a short overview of our entire transcription system and present its performance on transcriptions of several synthesized and real piano recordings. Results show that our approach represents a viable alternative to existing transcription systems.