Fundamentals of speech recognition
Fundamentals of speech recognition
Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Query by humming: musical information retrieval in an audio database
Proceedings of the third ACM international conference on Multimedia
Visualizing music and audio using self-similarity
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
Hierarchical filtering method for content-based music retrieval via acoustic input
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Warping indexes with envelope transforms for query by humming
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Exact indexing of dynamic time warping
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Scaling and time warping in time series querying
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Scaling and time warping in time series querying
The VLDB Journal — The International Journal on Very Large Data Bases
User specific training of a music search engine
MLMI'07 Proceedings of the 4th international conference on Machine learning for multimodal interaction
A novel approach based on fault tolerance and recursive segmentation to query by humming
AST/UCMA/ISA/ACN'10 Proceedings of the 2010 international conference on Advances in computer science and information technology
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This paper describes our practical query-by-humming system, SoundCompass, which is being used as a karaoke song selection system in Japan. First, we describe the fundamental techniques employed by SoundCompass such as normalization in a time-wise sense of music data, time-scalable and tone-shiftable time-series data, and making subsequences for efficient matching. Second, we describe techniques to make effective feature vectors based on real music data and do matching with them to develop accurate query-by-humming. Third, we share valuable knowledge that has been obtained through month's of practical use of Sound Compass. Fourth, we describe the latest version of the SoundCompass system that incorporates these new techniques and knowledge, as well as describe quantitative evaluations that prove the practicality of SoundCompass. The new system provides flexible and accurate similarity retrieval based on k-nearest neighbor searches with multi-dimensional spatial indices structured with multi-dimensional features vectors.