본 논문에서는 심층신경망 기반 음성 압축 모델의 지연 시간 단축에 따른 성능 변화를 분석하고, 보다 효과적인 저지연 모델 설계 방식을 제안한다. 최근 제안된 심층신경망 기반 오디오 및 음성 압축 모델들은 낮은 비트 전송률에서 기존의 신호처리 기반의 코덱 대비 뛰어난 복원 성능을 달성하였다. 그러나, 대부분의 심층신경망 기반 모델은 기존 신호처리 기반 오디오 코덱 과 유사한 지연시간을 필요로 한다. 따라서, 본 논문에서는 합성곱 신경망 기반 오디오 압축 모델의 지연시간을 단축시킴으로써 오는 성능 변화를 분석하고, 수용장의 확장 등 구조 변경을 통해 저지연 조건에서도 기존 모델과 유사한 복원 성능을 달성할 수 있음을 보인다.
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