IEICE TRANSACTIONS on Information
CQTXNet: A Modified Xception Network with Attention Modules for Cover Song Identification
Summary :
Song-level feature summarization is fundamental for the browsing, retrieval, and indexing of digital music archives. This study proposes a deep neural network model, CQTXNet, for extracting song-level feature summary for cover song identification. CQTXNet incorporates depth-wise separable convolution, residual network connections, and attention models to extend previous approaches. An experimental evaluation of the proposed CQTXNet was performed on two publicly available cover song datasets by varying the number of network layers and the type of attention modules.
- Publication
- IEICE TRANSACTIONS on Information Vol.E107-D No.1 pp.49-52
- Publication Date
- 2024/01/01
- Publicized
- 2023/10/02
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2023MUL0003
- Type of Manuscript
- Special Section LETTER (Special Section on Enriched Multimedia — Media technologies opening up the future —)
- Category
Authors
Jinsoo SEO
Gangneung-Wonju National University
Junghyun KIM
Electronics and Telecommunications Research Institute
Hyemi KIM
Electronics and Telecommunications Research Institute
Keyword
Latest Issue
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Jinsoo SEO, Junghyun KIM, Hyemi KIM, "CQTXNet: A Modified Xception Network with Attention Modules for Cover Song Identification" in IEICE TRANSACTIONS on Information,
vol. E107-D, no. 1, pp. 49-52, January 2024, doi: 10.1587/transinf.2023MUL0003.
Abstract: Song-level feature summarization is fundamental for the browsing, retrieval, and indexing of digital music archives. This study proposes a deep neural network model, CQTXNet, for extracting song-level feature summary for cover song identification. CQTXNet incorporates depth-wise separable convolution, residual network connections, and attention models to extend previous approaches. An experimental evaluation of the proposed CQTXNet was performed on two publicly available cover song datasets by varying the number of network layers and the type of attention modules.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2023MUL0003/_p
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@ARTICLE{e107-d_1_49,
author={Jinsoo SEO, Junghyun KIM, Hyemi KIM, },
journal={IEICE TRANSACTIONS on Information},
title={CQTXNet: A Modified Xception Network with Attention Modules for Cover Song Identification},
year={2024},
volume={E107-D},
number={1},
pages={49-52},
abstract={Song-level feature summarization is fundamental for the browsing, retrieval, and indexing of digital music archives. This study proposes a deep neural network model, CQTXNet, for extracting song-level feature summary for cover song identification. CQTXNet incorporates depth-wise separable convolution, residual network connections, and attention models to extend previous approaches. An experimental evaluation of the proposed CQTXNet was performed on two publicly available cover song datasets by varying the number of network layers and the type of attention modules.},
keywords={},
doi={10.1587/transinf.2023MUL0003},
ISSN={1745-1361},
month={January},}
Copy
TY - JOUR
TI - CQTXNet: A Modified Xception Network with Attention Modules for Cover Song Identification
T2 - IEICE TRANSACTIONS on Information
SP - 49
EP - 52
AU - Jinsoo SEO
AU - Junghyun KIM
AU - Hyemi KIM
PY - 2024
DO - 10.1587/transinf.2023MUL0003
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E107-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2024
AB - Song-level feature summarization is fundamental for the browsing, retrieval, and indexing of digital music archives. This study proposes a deep neural network model, CQTXNet, for extracting song-level feature summary for cover song identification. CQTXNet incorporates depth-wise separable convolution, residual network connections, and attention models to extend previous approaches. An experimental evaluation of the proposed CQTXNet was performed on two publicly available cover song datasets by varying the number of network layers and the type of attention modules.
ER -
English
