earticle

영어

In this paper, a machine-learning approach called Sparse Representation-based Classification (SRC) is used for automatic chord recognition in music signals. We extracted different Pitch Class Profile (PCP) features from raw audio and achieved sparse representation of classes via 1 -norm minimization on feature space to recognize 24 major and minor triads. This recognition model is evaluated on MIREX’09 dataset including the Beatles corpus. Our method is compared with various methods that entered the Music Information Retrieval Evaluation eXchange (MIREX) in 2014 towards the audio chord estimation of MIREX’09 dataset in Audio Chord Estimation task of MIREX. Experimental results demonstrate that our method has good accuracy rate in recognizing maj-min chords.