Making large-scale support vector machine learning practical
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
On the segmentation and analysis of continuous musical sound by digital computer.
On the segmentation and analysis of continuous musical sound by digital computer.
A discriminative model for polyphonic piano transcription
EURASIP Journal on Applied Signal Processing
A connectionist approach to automatic transcription of polyphonic piano music
IEEE Transactions on Multimedia
Multiple fundamental frequency estimation based on sparse representations in a structured dictionary
Digital Signal Processing
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Music transcription consists in transforming the musical content of audio data into a symbolic representation. The objective of this study is to investigate a transcription system for polyphonic piano, triggered by events corresponding to the played notes. The proposed method focuses on note events and their main characteristics: the attack instant, the pitch and the final instant. Onset detection exploits a binary time-frequency representation of the audio signal. Note classification and offset detection are based on constant Q transform (CQT) and support vector machines (SVMs). We present a collection of experiments using synthesized MIDI files and piano recordings, and compare the results with existing approaches.