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학술대회 Pretrained Network-based Sound Event Recognition for Audio Surveillance Applications
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저자
박수완, 김건우
발행일
202110
출처
International Conference on Information and Communication Technology Convergence (ICTC) 2021, pp.1306-1309
DOI
https://dx.doi.org/10.1109/ICTC52510.2021.9621184
협약과제
21HR2900, 5G기반 선제적 위험대응을 위한 예측적 영상보안 핵심기술 개발, 김건우
초록
Despite the recent surge in the demand on the audio recognition in surveillance systems, there are still many obstacles to readily use it in real environments such as legal restrictions on public data collection and difficulties in obtaining large-scale learning data. To overcome these problems, we propose an adaptive sound event recognition scheme based on a pre-trained network with the large-scale AudioSet, where PANNs-based CNN and SincNet[3] are employed to extract the audio features from log-mel spectrogram and waveform, respectively. Our experimental results show that the proposed method achieves mean average precision (mAP) of 0.415, which is slightly better than the best previous methods. Furthermore, we evaluate the performance of transfer leaning using a smaller amount of data collected by ourselves, considering dangerous situation scenarios in surveillance applications.
KSP 제안 키워드
Audio Features, Audio recognition, Audio surveillance, Data Collection, Data collected, Large-scale learning, Learning data, Public Data, Sound event recognition, Surveillance applications, Surveillance system