| 研究課題/領域番号 |
20K12080
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| 研究機関 | 会津大学 |
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研究代表者 |
MARKOV K 会津大学, コンピュータ理工学部, 教授 (80394998)
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| 研究分担者 |
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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| 研究期間 (年度) |
2020-04-01 – 2024-03-31
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| キーワード | cough recognition / cough monitoring / DNN audio analysis |
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| 研究実績の概要 |
During this year we built a high performance cough detection and monitoring system and successfully evaluated it using the data collected at FMU. There are 11 patients audio recordings in the dataset with highly irregular cough events time distribution. Out approach is to segment the input data into 10sec long segments and process each segment separately. Out model is based on fine tuned large audio model called HuBERT which can identify cough frames with high accuracy. In addition, we trained a special network which estimates the probability of each identified cough frame being the first, second, etc, in the couch event. This way, we can distinguish separate cough events even when they come right one after another within a long sequence of cough frames.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
3: やや遅れている
理由
Currently, we are conducting experiments involving other openly available methods and models for cough event detection and monitoring in order to compare the performance of our system and the other state-of-the-art systems. To achieve statistically significant experimental results, we are performing a 4-fold cross validation experiments and so far our system has obtained more than 90% F1 score.
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| 今後の研究の推進方策 |
After finishing the experiments, we plan to summarize the achievements of this project and publish them in a journal paper.
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| 次年度使用額が生じた理由 |
The amount for the next fiscal year will be used for publication fees.
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