Fundamentals of speech recognition
Fundamentals of speech recognition
The nature of statistical learning theory
The nature of statistical learning theory
Query by humming: musical information retrieval in an audio database
Proceedings of the third ACM international conference on Multimedia
Musical content-based retrieval: an overview of the Melodiscov approach and system
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
HMM-based musical query retrieval
Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries
Robust temporal and spectral modeling for query By melody
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
A hybrid graphical model for rhythmic parsing
Artificial Intelligence
Problems of music information retrieval in the real world
Information Processing and Management: an International Journal
Query by Rhythm: An Approach for Song Retrieval in Music Databases
RIDE '98 Proceedings of the Workshop on Research Issues in Database Engineering
The New Zealand Digital Library MELody in DEX
The New Zealand Digital Library MELody in DEX
Conventional and periodic N-grams in the transcription of drum sequences
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Sound onset detection by applying psychoacoustic knowledge
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
Automatic extraction of drum tracks from polyphonic music signals
WEDELMUSIC'02 Proceedings of the Second international conference on Web delivering of music
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Recent efforts in audio indexing and music information retrieval mostly focus on melody. If this is appropriate for polyphonic music signals, specific approaches are needed for systems dealing with percussive audio signals such as those produced by drums, tabla or djembé. In this article, we present a complete system allowing the management of a drum patterns (or drumloops) database. Queries in this database are formulated with spoken onomatopoeias-short meaningless words imitating the different sounds of the drumkit. The transcription task necessary to index the database is performed using Hidden Markov Models (HMM) and Support Vector Machines (SVM) and achieves a 86.4% correct recognition rate. The syllables of spoken queries are recognized and a relevant statistical model allows the comparison and alignment of the query with the rythmic sequences stored in the database, in order to provide a set of the most relevant drum loops.