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Conference Paper
SubSpectral Normalization를 적용한 음향 이벤트/장면 인식 알고리즘 성능 분석
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- Issue Date
- 2022-06
- Citation
- 대한전자공학회 학술 대회 (하계) 2022, pp.1346-1349
- Publisher
- 대한전자공학회
- Language
- Korean
- Type
- Conference Paper
- Abstract
- In this paper, we propose a novel acoustic event/scene recognition algorithm applied with SubSpectral Normalization that can maintain the importance of the unique characteristics of each frequency sub-band. Through the optimization of the existing algorithm, the model complexity is improved by 61% for Advanced GCRNN and 73.9% for Advanced Trident-ResCNN compared to the existing algorithm. Despite the improvement in model complexity, Advanced GCRNN improves accuracy by 0.8% and Advanced Trident-ResCNN improves F1-score by 1.9% compared to the existing algorithm in evaluation tests for 10 types of acoustic event/scene data.
- KSP Keywords
- Acoustic event, F1-score, Scene Recognition, model complexity, recognition algorithm, sub-band, unique characteristics