Open AccessNote‐based alignment using score‐driven non‐negative matrix factorisation for audio recordings
Author(s) -
Wang TienMing,
Tsai PeiYin,
Su Alvin W. Y.
Publication year - 2014
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/iet-spr.2012.0157
Subject(s) - computer science , matrix decomposition , speech recognition , factorization , non negative matrix factorization , matrix (chemical analysis) , artificial intelligence , algorithm , physics , eigenvalues and eigenvectors , materials science , quantum mechanics , composite material
This study presents a discussion on the task of score alignment, which properly aligns an audio recording with its corresponding score. Conventional methods have difficulty performing this task because of asynchrony in the recording of simultaneous notes in the score. A note‐based score alignment based on the pitch‐by‐time feature is proposed, called the piano‐roll feature, and it presents an approach for converting the audio spectrogram to a piano‐roll‐like feature. Score‐driven non‐negative matrix factorisation is then adopted in the transformation. Furthermore, this study also proposes pitch‐wise alignment considering each pitch sequence (i.e. the row of piano roll) separately. Results based on the MIDI‐Aligned Piano Sounds database show that approximately 88% of notes match their onsets, deviating from the ground truth by less than 50 ms. Other results based on SCREAM Music Annotation Project database that is a manual annotation project of commercial CD recordings are presented as well.