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
Intelligent Watermarking Techniques (Innovative Intelligence)
Intelligent Watermarking Techniques (Innovative Intelligence)
Semantic Music Recognition - Audio Identification beyond Fingerprinting
WEDELMUSIC '04 Proceedings of the Web Delivering of Music, Fourth International Conference
Score following: state of the art and new developments
NIME '03 Proceedings of the 2003 conference on New interfaces for musical expression
A Review of Audio Fingerprinting
Journal of VLSI Signal Processing Systems
Detecting harmonic change in musical audio
Proceedings of the 1st ACM workshop on Audio and music computing multimedia
IEICE - Transactions on Information and Systems
Perceptual audio hashing functions
EURASIP Journal on Applied Signal Processing
Information Retrieval for Music and Motion
Information Retrieval for Music and Motion
A System for the Automatic Identification of Music Works
ICIAPW '07 Proceedings of the 14th International Conference of Image Analysis and Processing - Workshops
Automatic Alignment of Music Performances with Scores Aimed at Educational Applications
AXMEDIS '08 Proceedings of the 2008 International Conference on Automated solutions for Cross Media Content and Multi-channel Distribution
Digital Watermarks for Audio Signals
ICMCS '96 Proceedings of the 1996 International Conference on Multimedia Computing and Systems
Chroma Binary Similarity and Local Alignment Applied to Cover Song Identification
IEEE Transactions on Audio, Speech, and Language Processing
Efficient Index-Based Audio Matching
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Audio, Speech, and Language Processing
Automatic identification of music works through audio matching
ECDL'07 Proceedings of the 11th European conference on Research and Advanced Technology for Digital Libraries
Music genre classification using LBP textural features
Signal Processing
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This paper describes a methodology for the automatic identification of audio recordings of ethnic music. The identification is based on an application of hidden Markov models (HMMs), which are automatically built from a representation of the music pieces to be identified. States of the HMMs are labeled with music events, and the transition and observation probabilities are directly computed from the information on the music piece. The recordings are modeled by a set of acoustic features that are computed according with the characteristics of the music events. Three alternative approaches, based on typical applications of HMMs, are proposed to perform the identification. Tests carried out on collections of recordings showed that the methodology can achieve good results, and the identification rate is high enough to suggest applications for automatic retrieval of metadata and for the identification of alternative recordings of the same piece.