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| Presentation | 2023-03-01 11:40 Predominant Instrument Recognition in Polyphonic Music Based on Transfer Learning with Vanilla ResNet-50 Lifan Zhong, Daisuke Saito, Nobuaki Minematsu (UTokyo)  EA2022-114 SIP2022-158 SP2022-78 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Instrument recognition is an active research field in MIR (Music Information Retrieval) and has great potential for real-world applications. While automatic recognition of single instruments, which may be done with monophonic musical phrases or isolated musical notes, has been well-studied, the recognition in polyphonic multi-instrumental music remains challenging due to its complexity. In polyphonic music, predominant instrument recognition is to identify the lead musical instruments in a given music sample. Recently, transfer learning from image classification tasks to audio tasks has shown promising results, thanks to the well-developed deep models and the large-scale, high-quality labeled ImageNet dataset. In this work, we investigate a transfer learning approach for predominant instrument recognition with ImageNet pre-trained vanilla ResNet-50. We obtain an F1-score of 0.680 and a label ranking average precision (LRAP) of 0.818, surpassing the previous baseline system (F1=0.602) by a significant margin. Moreover, an F1-score of 0.688 and an LRAP of 0.826 is achieved by the ensemble model. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | transfer learning / predominant instrument recognition / MIR / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 122, no. 387, EA2022-114, pp. 232-237, Feb. 2023. | |
| Paper # | EA2022-114 | |
| Date of Issue | 2023-02-21 (EA, SIP, SP) | |
| ISSN | Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | EA2022-114 SIP2022-158 SP2022-78 |
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| Conference Information | |
| Committee | SP IPSJ-SLP EA SIP |
| Conference Date | 2023-02-28 - 2023-03-01 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | EA |
| Conference Code | 2023-02-SP-SLP-EA-SIP |
| Language | English (Japanese title is available) |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Predominant Instrument Recognition in Polyphonic Music Based on Transfer Learning with Vanilla ResNet-50 |
| Sub Title (in English) | |
| Keyword(1) | transfer learning |
| Keyword(2) | predominant instrument recognition |
| Keyword(3) | MIR |
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| 1st Author's Name | Lifan Zhong |
| 1st Author's Affiliation | The University of Tokyo (UTokyo) |
| 2nd Author's Name | Daisuke Saito |
| 2nd Author's Affiliation | The University of Tokyo (UTokyo) |
| 3rd Author's Name | Nobuaki Minematsu |
| 3rd Author's Affiliation | The University of Tokyo (UTokyo) |
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| Speaker | Author-1 |
| Date Time | 2023-03-01 11:40:00 |
| Presentation Time | 20 minutes |
| Registration for | EA |
| Paper # | EA2022-114, SIP2022-158, SP2022-78 |
| Volume (vol) | vol.122 |
| Number (no) | no.387(EA), no.388(SIP), no.389(SP) |
| Page | pp.232-237 |
| #Pages | 6 |
| Date of Issue | 2023-02-21 (EA, SIP, SP) |
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