인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Abstract In this paper, general regression neural network (GRNN) with the input feature of Mel-frequency cepstrum coefficient (MFCC) is employed to automatically recognize the calls of leopard, ross, and weddell seals with widely overlapping living areas. As a feedforward network, GRNN has only one network parameter, i.e., spread factor. The recognition performance can be greatly improved by determining the spread factor based on the cross-validation method. This paper selects the audio data of the calls of the above three kinds of seals and compares the recognition performance of three machine learning models for inputting MFCC features and low-frequency analyzer and recorder (LOFAR) spectrum. The results show that at the same signal-to-noise ratio (SNR), the recognition result of the MFCC feature is better than that of the LOFAR spectrum, which is verified by statistical histogram. Compared with other models, GRNN for inputting MFCC features has better recognition performance and can still achieve effective recognition at low SNRs. Specifically, the accuracy is 97.36%, 93.44%, 92.00% and 88.38% for cases with an infinite SNR and SNR of 10, 5 and 0 dB, respectively. In particular, GRNN has the least training and testing time. Therefore, all results show that the proposed method has excellent performance for the seal call recognition.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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