A feature independent of bit rate for twinvq audio retrieval

Onishi, K.; Kobayakawa, Michihiro; Hoshi, Mamoru; Ohmori, Tadashi

doi:10.1109/icme.2001.1237714

Cited by 8 publications

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this section, we describe experiments and their results. To investigate the performance of mm-GNAT, we implemented three methods below and compared 20 100,000 non-uniform music 15) them:…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

mm-GNAT: Index Structure for Arbitrary Lp Norm

Onishi

Kobayakawa

Hoshi

2010

IPSJ Online Transactions

View full text Add to dashboard Cite

For fast ε-similarity search, various index structures have been proposed. Yi, et al. proposed a concept multi-modality support and suggested inequalities by which ε-similarity search by L 1 , L 2 and L∞ norm can be realized. We proposed an extended inequality which allows us to realize ε-similarity search by arbitrary Lp norm using an index based on Lq norm. In these investigations a search radius of a norm is converted into that of other norm. In this paper, we propose an index structure which allows search by arbitrary Lp norm, called mm-GNAT (multi-modality support GNAT), with the extention of ranges of GNAT, instead of extending the search radius. The index structure is based on GNAT (Geometric Near-neighbor Access Tree). We show that ε-similarity search by arbitrary Lp norm is realized on mm-GNAT. In addition, we performed search experiments on mm-GNAT with artificial data and music data. The results show that the search by arbitrary Lp norm is realized and the index structure has better search performance than Yi's method except for search by L 2 norm.

show abstract

Section: Methodsmentioning

confidence: 99%

“…Retrieval of music data is a hot topic 13) . We have proposed features for retrieval of music data 14), 15) .…”

Section: Application To Music Datamentioning

confidence: 99%