ABSTRACT
In many areas multimedia technology has made its way into mainstream. In the case of digital audio this is manifested in numerous online music stores having turned into profitable businesses. The widespread user adaption of digital audio both on home computers and mobile players show the size of this market. Thus, ways to automatically process and handle the growing size of private and commercial collections become increasingly important; along goes a need to make music interpretable by computers. The most obvious representation of audio files is their sound - there are, however, more ways of describing a song, for instance its lyrics, which describe songs in terms of content words. Lyrics of music may be orthogonal to its sound, and differ greatly from other texts regarding their (rhyme) structure. Consequently, the exploitation of these properties has potential for typical music information retrieval tasks such as musical genre classification; so far, there is a lack of means to efficiently combine these modalities. In this paper, we present findings from investigating advanced lyrics features such as the frequency of certain rhyme patterns, several parts-of-speech features, and statistic features such as words per minute (WPM). We further analyse in how far a combination of these features with existing acoustic feature sets can be exploited for genre classification and provide experiments on two test collections.
- S. Baumann, T. Pohle, and S. Vembu. Towards a socio-cultural compatibility of mir systems. In Proceedings of the 5th International Conference of Music Information Retrieval (ISMIR'04), pages 460--465, Barcelona, Spain, October 10-14 2004.Google Scholar
- E. Brochu, N. de Freitas, and K. Bao. The sound of an album cover: Probabilistic multimedia and IR. In C. M. Bishop and B. J. Frey, editors, Proceedings of the 9th International Workshop on Artificial Intelligence and Statistics, Key West, FL, USA, January 3-6 2003.Google Scholar
- W. B. Cavnar and J. M. Trenkle. N-gram-based text categorization. In Proceedings of the 3rd Annual Symposium on Document Analysis and Information Retrieval (SDAIR'94), pages 161--175, Las Vegas, USA, 1994.Google Scholar
- J. Downie. Annual Review of Information Science and Technology, volume 37, chapter Music Information Retrieval, pages 295--340. Information Today, Medford, NJ, 2003.Google Scholar
- J. Foote. An overview of audio information retrieval. Multimedia Systems, 7(1):2--10, 1999. Google ScholarDigital Library
- D. Iskandar, Y. Wang, M.-Y. Kan, and H. Li. Syllabic level automatic synchronization of music signals and text lyrics. In Proceedings of the ACM 14th International Conference on Multimedia (MM'06), pages 659--662, New York, NY, USA, 2006. Google ScholarDigital Library
- P. Knees, M. Schedl, T. Pohle, and G. Widmer. An Innovative Three-Dimensional User Interface for Exploring Music Collections Enriched with Meta-Information from the Web. In Proceedings of the ACM 14th International Conference on Multimedia (MM'06), pages 17--24, Santa Barbara, California, USA, October 23-26 2006. Google ScholarDigital Library
- P. Knees, M. Schedl, and G. Widmer. Multiple lyrics alignment: Automatic retrieval of song lyrics. In Proceedings of 6th International Conference on Music Information Retrieval (ISMIR'05), pages 564--569, London, UK, September 11-15 2005.Google Scholar
- T. Lidy and A. Rauber. Evaluation of feature extractors and psycho-acoustic transformations for music genre classification. In Proceedings of the 6th International Conference on Music Information Retrieval (ISMIR'05), pages 34--41, London, UK, September 11-15 2005.Google Scholar
- B. Logan, A. Kositsky, and P. Moreno. Semantic analysis of song lyrics. In Proceedings of the IEEE International Conference on Multimedia and Expo (ICME'04), pages 827--830, Taipei, Taiwan, June 27-30 2004.Google ScholarCross Ref
- J. P. G. Mahedero, Á,. Martínez, P. Cano, M. Koppenberger, and F. Gouyon. Natural language processing of lyrics. In Proceedings of the ACM 13th International Conference on Multimedia (MM'05), pages 475--478, New York, NY, USA, 2005. Google ScholarDigital Library
- R. Mayer, R. Neumayer, and A. Rauber. Rhyme and style features for musical genre classification by song lyrics. In Proceedings of the 9th International Conference on Music Information Retrieval (ISMIR'08), Philadelphia, PA, USA, September 14-18 2008. Accepted for publication.Google Scholar
- R. Neumayer and A. Rauber. Integration of text and audio features for genre classification in music information retrieval. In Proceedings of the 29th European Conference on Information Retrieval (ECIR'07), pages 724--727, Rome, Italy, April 2-5 2007. Google ScholarDigital Library
- R. Neumayer and A. Rauber. Multi-modal music information retrieval - visualisation and evaluation of clusterings by both audio and lyrics. In Proceedings of the 8th Conference Recherche d'Information Assistée par Ordinateur (RIAO'07), Pittsburgh, PA, USA, May 29th - June 1 2007.Google Scholar
- N. Orio. Music retrieval: A tutorial and review. Foundations and Trends in Information Retrieval, 1(1):1--90, September 2006. Google ScholarDigital Library
- E. Pampalk, A. Flexer, and G. Widmer. Hierarchical organization and description of music collections at the artist level. In Research and Advanced Technology for Digital Libraries ECDL'05, pages 37--48, 2005. Google ScholarDigital Library
- E. Pampalk, A. Rauber, and D. Merkl. Content-based Organization and Visualization of Music Archives. In Proceedings of the ACM 10th International Conference on Multimedia (MM'02), pages 570--579, Juan les Pins, France, December 1-6 2002. Google ScholarDigital Library
- A. Rauber, E. Pampalk, and D. Merkl. Using psycho-acoustic models and self-organizing maps to create a hierarchical structuring of music by musical styles. In Proceedings of the 3rd International Symposium on Music Information Retrieval (ISMIR'02), pages 71--80, Paris, France, October 13-17 2002.Google Scholar
- G. Salton. Automatic text processing - The Transformation, Analysis, and Retrieval of Information by Computer. Addison-Wesley Longman Publishing Co., Inc., 1989. Google ScholarDigital Library
- G. Tzanetakis and P. Cook. Marsyas: A framework for audio analysis. Organized Sound, 4(30):169--175, 2000. Google ScholarDigital Library
- G. Tzanetakis and P. Cook. Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing, 10(5):293--302, July 2002.Google ScholarCross Ref
- Y. Zhu, K. Chen, and Q. Sun. Multimodal content-based structure analysis of karaoke music. In Proceedings of the ACM 13th International Conference on Multimedia (MM'05), pages 638--647, Singapore, 2005. Google ScholarDigital Library
- E. Zwicker and H. Fastl. Psychoacoustics, Facts and Models, volume 22 of Series of Information Sciences. Springer, Berlin, 2 edition, 1999. Google ScholarDigital Library
Index Terms
- Combination of audio and lyrics features for genre classification in digital audio collections
Recommendations
Improving mood classification in music digital libraries by combining lyrics and audio
JCDL '10: Proceedings of the 10th annual joint conference on Digital librariesMood is an emerging metadata type and access point in music digital libraries (MDL) and online music repositories. In this study, we present a comprehensive investigation of the usefulness of lyrics in music mood classification by evaluating and ...
Music genre classification using MIDI and audio features
We report our findings on using MIDI files and audio features from MIDI, separately and combined together, for MIDI music genre classification. We use McKay and Fujinaga's 3-root and 9-leaf genre data set. In order to compute distances between MIDI ...
Music Genre Classification and Similarity Calculation Using Bass-Line Features
ISM '08: Proceedings of the 2008 Tenth IEEE International Symposium on MultimediaVarious studies on music information retrieval have been conducted. Most of them used low-level features such as spectral and cepstral features, which are effective to some extent but have a limit because they do not directly or clearly correspond to ...
Comments