A feedback framework for improved chord recognition based on NMF-based approximate note transcription

Abstract

This paper presents a feedback framework that can improve chord recognition for music audio signals by performing approximate note transcription with Bayesian non-negative matrix factorization (NMF) using prior knowledge on chords. Although the names and note compositions of chords are intrinsically linked with each other (e.g., C major chords are highly likely to include C, E, and G notes, and those notes are highly likely to be in C major chords), chord recognition and note transcription (multipitch analysis) have been studied independently. To solve this chicken-and-egg problem, our framework iterates chord recognition and approximate note transcription using each other's results. More specifically, we first perform approximate note transcription based on Bayesian NMF that forces basis spectra to respectively correspond to different semitonelevel pitches covering the whole range. We then execute chord recognition based on Bayesian hidden Markov models (HMMs) that use chroma features obtained from the activation patterns of those pitches. To improve note transcription, we again perform Bayesian NMF that encourages certain kinds of pitches in each chord region to be activated. Experimental results showed that our feedback framework gradually improved the accuracy of chord recognition.

Chord-aware automatic music transcription based on hierarchical Bayesian integration of acoustic and language models

“…Statistical methods of supervised chord recognition [5][6][7][8] are worth investigation for unsupervised music grammar induction. Rocher et al [5] attempted chord recognition from symbolic music by constructing a directed graph of possible chords and then calculating the optimal path.…”

“…They constructed an HMM whose latent variables are chord labels and whose observations are chroma vectors. Maruo et al [7] proposed a method that uses NMF for extracting reliable chroma features. Since these methods require labeled training data, the concept of chords is required in advance.…”

“…Many studies have been conducted for estimating chords from musical scores [5][6][7][8]. If chord labels are given as clues for multipitch estimation, musically appropriate piano rolls is expected to be obtained.…”

Guitar Chord Sensing and Recognition Using Multi-Task Learning and Physical Data Augmentation with Robotics

“…The log mel-spectrogram and constant Q transform (CQT) are widely used features in chord recognition [4,5]. Moreover, many research works have employed chroma representation in feature extraction [6,7].…”