Highlighting root notes in chord recognition using cepstral features and multi-task learning
Journal
2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
ISBN
9789881476821
Date Issued
2017-01-17
Author(s)
Abstract
A musical chord is usually described by its root note and the chord type. While a substantial amount of work has been done in the field of music information retrieval (MIR) to automate chord recognition, the role of root notes in this task has seldom received specific attention. In this paper, we present a new approach and empirical studies demonstrating improved accuracy in chord recognition by properly highlighting the information of the root notes. In the signal level, we propose to combine spectral features with features derived from the cepstrum to improve the identification of low pitches, which usually correspond to the root notes. In the model level, we propose a multi-task learning framework based on the neural nets to jointly consider chord recognition and root note recognition in training. We found that the improved accuracy can be attributed to better information about the sub-harmonics of the notes, and the emphasis of root notes in recognizing chords.
SDGs
Type
conference paper