Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Determination of Nonprototypical Valence and Arousal in Popular Music: Features and Performances | Hindawi Publishing Corporation EURASIP Journal on Audio Speech and Music Processing Volume 2010 Article ID 735854 19 pages doi 2010 735854 Research Article Determination of Nonprototypical Valence and Arousal in Popular Music Features and Performances Bjorn Schuller Johannes Dorfner and Gerhard Rigoll Institute for Human-Machine Communication Technische Universitat Munchen Munchen 80333 Germany Correspondence should be addressed to Bjorn Schuller schuller@ Received 27 May 2009 Revised 4 December 2009 Accepted 8 January 2010 Academic Editor Liming Chen Copyright 2010 Bjorn Schuller et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Mood of Music is among the most relevant and commercially promising yet challenging attributes for retrieval in large music collections. In this respect this article first provides a short overview on methods and performances in the field. While most past research so far dealt with low-level audio descriptors to this aim this article reports on results exploiting information on middlelevel as the rhythmic and chordal structure or lyrics of a musical piece. Special attention is given to realism and nonprototypicality of the selected songs in the database all feature information is obtained by fully automatic preclassification apart from the lyrics which are automatically retrieved from on-line sources. Further more instead of exclusively picking songs with agreement of several annotators upon perceived mood a full collection of 69 double CDs or 2 648 titles respectively is processed. Due to the severity of this task different modelling forms in the arousal and valence space are investigated and relevance per feature group is reported. 1. Introduction Music is ambient. Audio encoding has enabled us to digitise our musical heritage and new songs are released .
