Sparse Modeling of Magnitude and Phase-Derived Spectra for Playing Technique Classification

Li Su, Hsin-Ming Lin, Yi-Hsuan Yang
2014 IEEE/ACM Transactions on Audio Speech and Language Processing  
Automatic recognition of guitar playing techniques is challenging as it is concerned with subtle nuances of guitar timbres. In this paper, we investigate this research problem by a comparative study on the performance of features extracted from the magnitude spectrum, cepstrum and phase derivatives such as group-delay function (GDF) and instantaneous frequency deviation (IFD) for classifying the playing techniques of electric guitar recordings. We consider up to 7 distinct playing techniques of
more » ... electric guitar and create a new individual-note dataset comprising of 7 types of guitar tones for each playing technique. The dataset contains 6,580 clips and 11,928 notes. Our evaluation shows that sparse coding is an effective means of mining useful patterns from the primitive time-frequency representations and that combining the sparse representations of logarithm cepstrum, GDF and IFD leads to the highest average F-score of 71.7%. Moreover, from analyzing the confusion matrices we find that cepstral and phase features are particularly important in discriminating highly similar techniques such as pull-off, hammer-on and bending. We also report a preliminary study that demonstrates the potential of the proposed methods in automatic transcription of real-world electric guitar solos.
doi:10.1109/taslp.2014.2362006 fatcat:zr4z5dm4irdd3j7h5zd26uc5by