Fundamentals of speech recognition
Fundamentals of speech recognition
Towards the digital music library: tune retrieval from acoustic input
Proceedings of the first ACM international conference on Digital libraries
Automatic Segmentation of Acoustic Musical Signals Using Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-based retrieval for music collections
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Melodic matching techniques for large music databases
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
Evaluation of a simple and effective music information retrieval method
SIGIR '00 Proceedings of the 23rd 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
The MUSART Testbed for Query-by-Humming Evaluation
Computer Music Journal
Query by humming with the VocalSearch system
Communications of the ACM - Music information retrieval
Proceedings of the 1st ACM workshop on Audio and music computing multimedia
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
Learning for efficient retrieval of structured data with noisy queries
Proceedings of the 24th international conference on Machine learning
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Similarity clustering of music files according to user preference
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
User specific training of a music search engine
MLMI'07 Proceedings of the 4th international conference on Machine learning for multimodal interaction
Benchmarking dynamic time warping for music retrieval
Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments
Model-based search in large time series databases
Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments
Hum-a-song: a subsequence matching with gaps-range-tolerances query-by-humming system
Proceedings of the VLDB Endowment
A survey of query-by-humming similarity methods
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
Query by humming: Automatically building the database from music recordings
Pattern Recognition Letters
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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 data-base. The top pieces are returned to the user in order of similarity to the sung theme. We describe, in detail, two different approaches to measuring similarity between database themes and the sung query. In the first, queries are compared to database themes using standard string-alignment algorithms. Here, similarity between target and query is determined by edit cost. In the second approach, pieces in the database are represented as hidden Markov models (HMMs). In this approach, the query is treated as an observation sequence and a target is judged similar to the query if its HMM has a high likelihood of generating the query. In this article we report our approach to the construction of a target database of themes, encoding, and transcription of user queries, and the results of preliminary experimentation with a set of sung queries. Our experiments show that while no approach is clearly superior to the other system, string matching has a slight advantage. Moreover, neither approach surpasses human performance.