The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Dans cet article, des méthodes de récupération et de catégorisation de sons visualisés utilisant un moteur de recherche d'images basé sur des fonctionnalités ont été évaluées dans le but d'interroger efficacement les scènes vidéo. Les modèles codés par couleur du spectrogramme sonore sont adoptés comme indice sonore visualisé. Des expériences de catégorisation sonore ont été menées à l'aide de bases de données sonores visualisées comprenant la parole, le chant des oiseaux, les sons musicaux, le gazouillis des insectes et la bande sonore d'une vidéo sportive. Les résultats des expériences de récupération montrent que le moteur de recherche d'images simple basé sur des caractéristiques peut être utilisé efficacement pour la récupération et la catégorisation des sons visualisés. Les résultats d’expériences de catégorisation impliquant des humains montrent qu’après une brève formation, les humains peuvent au moins effectuer une catégorisation grossière. Ces résultats suggèrent que l’utilisation du son visualisé peut être une méthode efficace pour une requête de scène vidéo efficace.
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Katsunobu FUSHIKIDA, Yoshitsugu HIWATARI, Hideyo WAKI, "Visualized Sound Retrieval and Categorization Using a Feature-Based Image Search Engine" in IEICE TRANSACTIONS on Information,
vol. E83-D, no. 11, pp. 1978-1985, November 2000, doi: .
Abstract: In this paper, visualized sound retrieval and categorization methods using a feature-based image search engine were evaluated aiming at efficient video scene query. Color-coded patterns of the sound spectrogram are adopted as the visualized sound index. Sound categorization experiments were conducted using visualized sound databases including speech, bird song, musical sounds, insect chirping, and the sound-track of sports video. The results of the retrieval experiments show that the simple feature-based image search engine can be effectively used for visualized sound retrieval and categorization. The results of categorization experiments involving humans show that after brief training humans can at least do rough categorization. These results suggest that using visualized sound can be effective method for an efficient video scene query.
URL: https://global.ieice.org/en_transactions/information/10.1587/e83-d_11_1978/_p
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@ARTICLE{e83-d_11_1978,
author={Katsunobu FUSHIKIDA, Yoshitsugu HIWATARI, Hideyo WAKI, },
journal={IEICE TRANSACTIONS on Information},
title={Visualized Sound Retrieval and Categorization Using a Feature-Based Image Search Engine},
year={2000},
volume={E83-D},
number={11},
pages={1978-1985},
abstract={In this paper, visualized sound retrieval and categorization methods using a feature-based image search engine were evaluated aiming at efficient video scene query. Color-coded patterns of the sound spectrogram are adopted as the visualized sound index. Sound categorization experiments were conducted using visualized sound databases including speech, bird song, musical sounds, insect chirping, and the sound-track of sports video. The results of the retrieval experiments show that the simple feature-based image search engine can be effectively used for visualized sound retrieval and categorization. The results of categorization experiments involving humans show that after brief training humans can at least do rough categorization. These results suggest that using visualized sound can be effective method for an efficient video scene query.},
keywords={},
doi={},
ISSN={},
month={November},}
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TY - JOUR
TI - Visualized Sound Retrieval and Categorization Using a Feature-Based Image Search Engine
T2 - IEICE TRANSACTIONS on Information
SP - 1978
EP - 1985
AU - Katsunobu FUSHIKIDA
AU - Yoshitsugu HIWATARI
AU - Hideyo WAKI
PY - 2000
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E83-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2000
AB - In this paper, visualized sound retrieval and categorization methods using a feature-based image search engine were evaluated aiming at efficient video scene query. Color-coded patterns of the sound spectrogram are adopted as the visualized sound index. Sound categorization experiments were conducted using visualized sound databases including speech, bird song, musical sounds, insect chirping, and the sound-track of sports video. The results of the retrieval experiments show that the simple feature-based image search engine can be effectively used for visualized sound retrieval and categorization. The results of categorization experiments involving humans show that after brief training humans can at least do rough categorization. These results suggest that using visualized sound can be effective method for an efficient video scene query.
ER -