Melodic matching techniques for large music databases
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
Warping indexes with envelope transforms for query by humming
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Name that tune: a pilot study in finding a melody from a sung query
Journal of the American Society for Information Science and Technology
The MUSART Testbed for Query-by-Humming Evaluation
Computer Music Journal
Challenging Uncertainty in Query by Humming Systems: A Fingerprinting Approach
IEEE Transactions on Audio, Speech, and Language Processing
Genre classification of symbolic music with SMBGT
Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments
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We present "Hum-a-song", a system built for music retrieval, and particularly for the Query-By-Humming (QBH) application. According to QBH, the user is able to hum a part of a song that she recalls and would like to learn what this song is, or find other songs similar to it in a large music repository. We present a simple yet efficient approach that maps the problem to time series subsequence matching. The query and the database songs are represented as 2-dimensional time series conveying information about the pitch and the duration of the notes. Then, since the query is a short sequence and we want to find its best match that may start and end anywhere in the database, subsequence matching methods are suitable for this task. In this demo, we present a system that employs and exposes to the user a variety of state-of-the-art dynamic programming methods, including a newly proposed efficient method named SMBGT that is robust to noise and considers all intrinsic problems in QBH; it allows variable tolerance levels when matching elements, where tolerances are defined as functions of the compared sequences, gaps in both the query and target sequences, and bounds the matching length and (optionally) the minimum number of matched elements. Our system is intended to become open source, which is to the best of our knowledge the first non-commercial effort trying to solve QBH with a variety of methods, and that also approaches the problem from the time series perspective.