청각장애인은 일상생활에서 위험 소리를 듣지 못하여 위험에 많이 노출되고 있으며, 이를 해결하기위해서 위험 소리를 인지하여 청각장애인에게 알려주는 기술이 필요하다. 최근 딥러닝을 적용하여음향 이벤트를 분류하는 기술에 대한 연구는 진행되고 있지만, 위험 소리에 특화된 데이터 셋이 존재하지 않아 위험 소리를 분류하는 기술은 연구가 많이 진행되고 있지 않다. 따라서 본 논문에서는 10 개의 범주에 대하여 26시간 규모의 위험 소리 데이터 셋을 구축하였다. 그리고 최신 합성곱 신경망모델인 SE-ResNeXt을 사용하는 위험 소리 분류 방법을 제안하였다. 마지막으로 본 연구에서 구축한 위험 소리 데이터 셋을 사용하여 기존의 방법과 제안 방법의 분류 성능을 분석하였으며, 본 논문에서 제안한 SE-ResNeXt 기반의 위험 소리 분류 방법이 기존의 방법보다 뛰어난 분류 성능을 가지는 것을 실험 결과로 확인하였다.
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