String searching algorithms
Query by humming: musical information retrieval in an audio database
Proceedings of the third ACM international conference on Multimedia
Melodic matching techniques for large music databases
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
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)
A practical query-by-humming system for a large music database
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Communications of the ACM
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Robust Polyphonic Music Retrieval with N-grams
Journal of Intelligent Information Systems
Efficient Theme and Non-Trivial Repeating Pattern Discovering in Music Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Approximate matching algorithms for music information retrieval using vocal input
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
CompositeMap: a novel framework for music similarity measure
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
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In recent years, the need to efficiently store and retrieve large amounts of musical information has increased. In this paper, we design and implement a Query-By-Humming (QBH) system, which can retrieve melodies similar to users' humming. To make this QBH system efficient, the following three methods were proposed. First, we convert the melodies to be indexed into the corresponding strings, in order to increase search speed. The conversion method is designed to tolerate the errors involved in humming. Second, we extract significant melodies from music and then build a couple of indexes from them. For this task, we propose reliable methods for extracting melodies that occur frequently and for melodies that begin after a long rest. Third, we propose a three-step index searching method for minimizing database access. Through the experiments with a real-world data set, it was verified that this system has noticeable improvements over the N-gram approach.