Yi-Hsuan Yang
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(NOTE: Please come here for a chronological order of the papers)


This book provides a comprehensive introduction of the research on modeling human's emotion perception of music, a research topic that emerges in the face of the ever-increasing amount of digital music content. Automatic recognition of the perceived emotion of music allows users to retrieve and organize their music collections in an emotion-based fashion, which is more content-centric than conventional metadata-based methods. Building such a music emotion recognition system, however, is challenging because of the subjective nature of emotion perception. One needs to deal with issues such as the reliability of ground truth data and the difficulty of evaluating the prediction result, which do not exist in other pattern recognition problems such as face recognition and speech recognition. This book provides the detail of the methods that have been developed to address these issues.

Book Chapters

Journal Papers

·       Y.-P. Lin, P.-K. Jao, and Y.-H. Yang, “
"Improving cross-day EEG-based emotion classification using robust principal component analysis,"
Frontiers in Computational Neuroscience (
(paper, bib)


Conference Papers

Music and emotion


Music auto-tagging and classification

Source separation

Music transcription


Music Generation


·       P.-K. Jao, Y.-P. Lin, Y.-H. Yang, and T.-P. Jung,
"Using robust principal component analysis to alleviate day-to-day variability in EEG based emotion classification,"
in Proc. Annual Int. Conf. IEEE Engineering in Medicine and Biology Society 2015 (EMBC’15), pp. 570-573.

Music and video

Structure Analysis

Chord recognition


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