Query-by-Humming on Internet

Kosugi, Naoko; Nagata, Hajime; Nakanishi, Tadashi

doi:10.1007/978-3-540-45227-0_58

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…To achieve scalability and high-speed similarity retrieval, we built a search engine with distributed indices, which we call Keren [7]. All queries are processed in parallel with multiple threads that are generated when Keren is initialized.…”

Section: Retrieval Engine and Similarity Measurementmentioning

confidence: 99%

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SoundCompass

Kosugi¹,

Sakurai²,

Morimoto³

2004

Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data

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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 SoundCompass. 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 feature vectors.

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Section: Retrieval Engine and Similarity Measurementmentioning

confidence: 99%

“…The previous version of SoundCompass [7,8] needed a metronome to time-normalize the humming data. The Sound-Compass does not need one because song data and hummings are time-normalized automatically.…”

Section: Is a Metronome Necessary?mentioning

confidence: 99%

SoundCompass

Kosugi¹,

Sakurai²,

Morimoto³

2004

Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data

Self Cite

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“…W HEN users want to locate specific music data from the huge volumes contained in music databases, they usually input bibliographic keywords, such as title and artist. When they do not have any bibliographic keywords, they can use content-based music-retrieval systems that enable them to find data by singing the song, typing parts of the lyrics, or humming the tune [1], [2], [3], [4]. However, these systems are ineffective if they do not specify the exact music data they want to find.…”

Section: Introductionmentioning

confidence: 99%

A Natural Language Dialogue System for Impression-based Music Retrieval

Kumamoto¹

2010

Polibits

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Impression-based music retrieval is the best way to find pieces of music that suit the preferences, senses, or mental states of users. A natural language interface (NLI) is more useful and effective than a graphical user interface for impression-based music retrieval since an NLI interprets users' spontaneous input sentences to represent musical impressions and generates query vectors for music retrieval. Existing impression-based music retrieval systems, however, have no dialogue capabilities for modifying the most recently used query vector. We evaluated a natural language dialogue system we developed that deals not only with 164 impression words but also with 14 comparative expressions, such as "a little more" and "more and more," and, if necessary, modifies the most recently used query vector through a dialogue. We also evaluated performance using 35 participants to determine the effectiveness of our dialogue system.

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Design and Evaluation of a Music Retrieval Scheme That Adapts to the User’s Impressions

Kumamoto

2005

User Modeling 2005

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Query-by-Humming on Internet

Cited by 7 publications

References 6 publications

SoundCompass

SoundCompass

A Natural Language Dialogue System for Impression-based Music Retrieval

Design and Evaluation of a Music Retrieval Scheme That Adapts to the User’s Impressions

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