Matching Pursuit With Damped Sinusoids
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 3 - Volume 3
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Sinusoidal modeling using frame-based perceptually weighted matching pursuits
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 05
A discriminative model for polyphonic piano transcription
EURASIP Journal on Applied Signal Processing
Harmonic decomposition of audio signals with matching pursuit
IEEE Transactions on Signal Processing
Sparse signal reconstruction from limited data using FOCUSS: are-weighted minimum norm algorithm
IEEE Transactions on Signal Processing
Specmurt Analysis of Polyphonic Music Signals
IEEE Transactions on Audio, Speech, and Language Processing
Sparse and structured decompositions of signals with the molecular matching pursuit
IEEE Transactions on Audio, Speech, and Language Processing
Automatic Piano Transcription Using Frequency and Time-Domain Information
IEEE Transactions on Audio, Speech, and Language Processing
Instrument-Specific Harmonic Atoms for Mid-Level Music Representation
IEEE Transactions on Audio, Speech, and Language Processing
A connectionist approach to automatic transcription of polyphonic piano music
IEEE Transactions on Multimedia
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Harmonic decompositions are a powerful tool dealing with polyphonic music signals in some potential applications such as music visualization, music transcription and instrument recognition. The usefulness of a harmonic decomposition relies on the design of a proper harmonic dictionary. Music scene-adaptive harmonic atoms have been used with this purpose. These atoms are adapted to the musical instruments and to the music scene, including aspects related with the venue, musician, and other relevant acoustic properties. In this paper, an unsupervised process to obtain music scene-adaptive spectral patterns for each MIDI-note is proposed. Furthermore, the obtained harmonic dictionary is applied to note-event detection with matching pursuits. In the case of a music database that only consists of one-instrument signals, promising results (high accuracy and low error rate) have been achieved for note-event detection.