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We have created a system for music search and retrieval. A user sings a theme from the desired piece of music. The sung theme (query) is converted into a sequence of pitch-intervals and rhythms. This sequence is compared to musical themes (targets) stored in a database. The top pieces are returned to the user in order of similarity to the sung theme. We(More)
We have investigated the performance of a hidden Markov model QBH retrieval system on a large musical database. The database is synthetic, generated from statistics gleaned from our (smaller) database of musical excerpts from various genres. This paper reports the performance of several variations of our retrieval system against different types of synthetic(More)
We have created a system for music search and retrieval. A user sings a theme from the desired piece of music. Pieces in the database are represented as hidden Markov models (HMMs). The query is treated as an observation sequence and a piece is judged similar to the query if its HMM has a high likelihood of generating the query. The top pieces are returned(More)
Time series representations are common in MIR applications such as query-by-humming, where a sung query might be represented by a series of 'notes' for database retrieval. While such a transcription into a sequence of (pitch, duration) pairs is convenient and musically intuitive , there is no evidence that it is an optimal representation. The present work(More)
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