Search (2 results, page 1 of 1)

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Abstract

The critical role of artificial intelligence in a digital library is to provide an infrastructure that accomodates extreme heterogeneity of content and function, and supports continual extension and evolution in both categories as well. The University of Michigan Digital Library project, USA, aims to satisfy these criteria with an architecture based on distributed information agents. Describes the agents used and indicates the role of agent communication languages

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Abstract

Query-by-humming systems offer content-based searching for melodies and require no special musical training or knowledge. Many such systems have been built, but there has not been much useful evaluation and comparison in the literature due to the lack of shared databases and queries. The MUSART project testbed allows various search algorithms to be compared using a shared framework that automatically runs experiments and summarizes results. Using this testbed, the authors compared algorithms based on string alignment, melodic contour matching, a hidden Markov model, n-grams, and CubyHum. Retrieval performance is very sensitive to distance functions and the representation of pitch and rhythm, which raises questions about some previously published conclusions. Some algorithms are particularly sensitive to the quality of queries. Our queries, which are taken from human subjects in a realistic setting, are quite difficult, especially for n-gram models. Finally, simulations on query-by-humming performance as a function of database size indicate that retrieval performance falls only slowly as the database size increases.