A simple rule-based part of speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
LyricAlly: automatic synchronization of acoustic musical signals and textual lyrics
Proceedings of the 12th annual ACM international conference on Multimedia
Singing voice detection in popular music
Proceedings of the 12th annual ACM international conference on Multimedia
Natural language processing of lyrics
Proceedings of the 13th annual ACM international conference on Multimedia
MusicStory: a personalized music video creator
Proceedings of the 13th annual ACM international conference on Multimedia
Syllabic level automatic synchronization of music signals and text lyrics
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Intelligent Algorithms in Ambient and Biomedical Computing (Philips Research Book Series)
Intelligent Algorithms in Ambient and Biomedical Computing (Philips Research Book Series)
Towards automatic retrieval of album covers
ECIR'06 Proceedings of the 28th European conference on Advances in Information Retrieval
Analysis of the meter of acoustic musical signals
IEEE Transactions on Audio, Speech, and Language Processing
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We present a method to synchronize popular music with its lyrics at the stanza level. First we apply an algorithm to segment audio content into harmonically similar and/or contrasting progressions, i.e. the stanzas. We map the stanzas found to a sequence of labels, where stanzas with a similar progression are mapped to the same label. The lyrics are analyzed as well to compute a second sequence of labels. Using dynamic programming, an optimal match is found between the two sequences, resulting in a stanza-level synchronization of the lyrics and the audio. The synchronized lyrics can be used to compute a synchronized slide show to accompany the music, where the images are retrieved using the lyrics. For an additional enrichment of the experience, colored light effects are synchronized with the music that are computed from the sets of images. The song segmentation can be done reliably, while the mapping of the audio segments and lyrics gives encouraging results.