A methodology for implementing highly concurrent data objects
ACM Transactions on Programming Languages and Systems (TOPLAS)
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Enriching music with synchronized lyrics, images and colored lights
Proceedings of the 1st international conference on Ambient media and systems
Combination of audio and lyrics features for genre classification in digital audio collections
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Toward Multi-modal Music Emotion Classification
PCM '08 Proceedings of the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Integration of text and audio features for genre classification in music information retrieval
ECIR'07 Proceedings of the 29th European conference on IR research
An integrated music video browsing system for personalized television
Expert Systems with Applications: An International Journal
Large Scale Semantic Access to Content (Text, Image, Video, and Sound)
Exploring the music similarity space on the web
ACM Transactions on Information Systems (TOIS)
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
A survey of music similarity and recommendation from music context data
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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We report experiments on the use of standard natural language processing (NLP) tools for the analysis of music lyrics. A significant amount of music audio has lyrics. Lyrics encode an important part of the semantics of a song, therefore their analysis complements that of acoustic and cultural metadata and is fundamental for the development of complete music information retrieval systems. Moreover, a textual analysis of a song can generate ground truth data that can be used to validate results from purely acoustic methods. Preliminary results on language identification, structure extraction, categorization and similarity searches suggests that a lot of profit can be gained from the analysis of lyrics.