SoundCompass: a practical query-by-humming system; normalization of scalable and shiftable time-series data and effective subsequence generation

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.