Automatic Music Transcription

Research Overview

Automatic music transcription (AMT) aims to extract a musical notation in a form of symbolic data from a recording of a music signal automatically. It encompasses a wide range of tasks in the music signal processing, including note onset detection, pitch estimation, and multi-instrument separation. For a full transcription, by means of the complete conversion from an audio signal to a “piano-roll” representation, two essential data of “onset/offset” and “pitch” should be obtained from both temporal and spectral analysis. In addition, our recent studies have been extended to find new features that imply musical expressions, such as a singing voice and varied playing styles of musical instruments.

Publications

  • S. Chang, K. Lee, “A pairwise approach to simultaneous onset/offset detection for singing voice using correntropy,” in Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), 2014. pdf
  • H. Heo, D. Sung, K. Lee, “Note Onset Detection based on Harmonic Cepstrum Regularity,” in Proc. IEEE Int. Conf. Multimedia and Expo (ICME), 2013. pdf

Datasets

  • Note-level Singing Voice Dataset (beta)
  • Project Members

    Hoon Heo, Sungkyun Chang, Dooyong Sung, Yoonchang Han