Towards the digital music library: tune retrieval from acoustic input
Proceedings of the first ACM international conference on Digital libraries
Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Content-based retrieval for music collections
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
HMM-based musical query retrieval
Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries
Efficient Index Structures for String Databases
Proceedings of the 27th International Conference on Very Large Data Bases
A Metric Index for Approximate String Matching
LATIN '02 Proceedings of the 5th Latin American Symposium on Theoretical Informatics
Warping indexes with envelope transforms for query by humming
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Algorithms for Chordal Analysis
Computer Music Journal
OASIS: an online and accurate technique for local-alignment searches on biological sequences
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Proceedings of the 1st ACM workshop on Audio and music computing multimedia
A comparative evaluation of search techniques for query-by-humming using the MUSART testbed
Journal of the American Society for Information Science and Technology
Music information retrieval from a singing voice using lyrics and melody information
EURASIP Journal on Applied Signal Processing
User specific training of a music search engine
MLMI'07 Proceedings of the 4th international conference on Machine learning for multimodal interaction
A survey of query-by-humming similarity methods
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
Genre classification of symbolic music with SMBGT
Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments
Seven problems that keep MIR from attracting the interest of cognition and neuroscience
Journal of Intelligent Information Systems
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We propose a model for errors in sung queries, a variant of the hidden Markov model (HMM). This is a solution to the problem of identifying the degree of similarity between a (typically error-laden) sung query and a potential target in a database of musical works, an important problem in the field of music information retrieval. Similarity metrics are a critical component of "query-by-humming" (QBH) applications which search audio and multimedia databases for strong matches to oral queries. Our model comprehensively expresses the types of error or variation between target and query: cumulative and noncumulative local errors, transposition, tempo and tempo changes, insertions, deletions and modulation. The model is not only expressive, but automatically trainable, or able to learn and generalize from query examples. We present results of simulations, designed to assess the discriminatory potential of the model, and tests with real sung queries, to demonstrate relevance to real-world applications.