| Project/Area Number |
20K12080
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 62020:Web informatics and service informatics-related
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| Research Institution | The University of Aizu |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
松井 知子 統計数理研究所, モデリング研究系, 教授 (10370090)
齋藤 純平 福島県立医科大学, 医学部, 講師 (50332929)
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| Project Period (FY) |
2020-04-01 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2022: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2021: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2020: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
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| Keywords | cough monitoring / health informatics / audio event detection / cough recognition / DNN audio analysis / machine learning / deep learning |
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| Outline of Research at the Start |
現在,我々の知る限り咳嗽の種類(痰がらみの咳嗽,痰を伴わない咳嗽)ごとに咳嗽頻度を抽出する完全な自動咳監視サービスシステムは存在しない。本研究において、世界に先駆けて本システムを開発する。本研究成果は、最先端の統計・機械学習技術に基づくシステムとしての科学的価値と独創性を持つだけでなく、実用上容易に(商業的に)複製することができるため、 病院や一般開業医に提供できる。本モニタリングシステムが日常診療に導入できれば、本邦の呼吸器疾患で最も多い症状である咳嗽の簡便かつ迅速な診断が可能なるだけではなく、客観的かつ正確な治療効果が判定も行えるため、患者にあった個別化医療を提供することができると考える.
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| Outline of Final Research Achievements |
In this research, we propose a novel deep learning system for the efficient detection and monitoring of cough events in audio recordings. First, we perform voice activity detection to eliminate audio silences. Next, we employ a cough classification to identify the presence of cough. Finally, we implement cough event detection using a high-performance classification-regression fusion method. Our approach is unique in several notable ways: 1) we incorporate a teacher-student framework for the training of our detection model, 2) instead of relying on specific audio features such as MFCC or Mel Spectrogram, our end-to-end system takes the raw audio signal directly as input and outputs the cough boundary timings, 3) the proposed method is general enough to be used for various other sound event monitoring tasks. The comparative cough monitoring evaluation of our approach against systems such as the Leicester Cough Monitor demonstrates our method's superiority by achieving the lowest error.
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| Academic Significance and Societal Importance of the Research Achievements |
The proposed cough detection and monitoring approach has the potential to impact various domains beyond respiratory health, such as urban planning, environmental monitoring, and animal welfare. The methodology is independent and adaptable to any sound event detection task requiring counting events.
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