Fundamentals of speech recognition
Fundamentals of speech recognition
Automatic Segmentation of Acoustic Musical Signals Using Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Topic segmentation: algorithms and applications
Topic segmentation: algorithms and applications
Advances in domain independent linear text segmentation
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Semantic Music Recognition - Audio Identification beyond Fingerprinting
WEDELMUSIC '04 Proceedings of the Web Delivering of Music, Fourth International Conference
A Review of Audio Fingerprinting
Journal of VLSI Signal Processing Systems
Digital Watermarks for Audio Signals
ICMCS '96 Proceedings of the 1996 International Conference on Multimedia Computing and Systems
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The availability of large music repositories poses challenging research problems, which are also related to the identification of different performances of music scores. This paper presents a methodology for music identification based on hidden Markov models. In particular, a statistical model of the possible performances of a given score is built from the recording of a single performance. To this end, the audio recording undergoes a segmentation process, followed by the extraction of the most relevant features of each segment. The model is built associating a state for each segment and by modeling its emissions according to the computed features. The approach has been tested with a collection of orchestral music, showing good results in the identification and tagging of acoustic performances.