Music and Audio Computing Lab

Score and Performace Alignment

Main Contributors: Taegyun Kwon, Dasaem Jeong, and Jiyun Park

In classifical piano music, pianists play a piece of music with different styles and interpretations. This topic explores machine learning methods to extract performance information such as note onset, duration and velocity from piano music recordings and analyzes different nuances among pianists.

Audio-to-Score Alignment

In classifical piano music, music scores are usually available on the player's side. Aligning the music score with different renditions of performances allows us to compare different play styles in terms of tempo, dynamics, phrasing and articulation. We proposed a method for the audio-to-score alignment using a polyphonic piano transcription algorithm, and also designed a web-based visualization system to intuitively show different renditions of the same piano piece.

PerformScore: Web-based Piano Score and Performance Analysis Visualization System [link]

Related Publications

  • Audio-to-Score Alignment Of Piano Music Using RNN-based Automatic Music Transcription
    Taegyun Kwon, Dasaem Jeong and Juhan Nam
    Proceedings of the 14th Sound and Music Computing Conference (SMC), 2017 [pdf] [demo]
  • PerformScore: Toward Performance Visualization With the Score on the Web Browser
    Dasaem Jeong, Taegyun Kwon, Chaelin Park, and Juhan Nam
    Late Breaking Demo in the 18th International Society for Musical Information Retrieval Conference (ISMIR), 2017 [pdf]