활어 개체어의 광대역 음향산란신호로부터 어종식별을 위한 시간-주파수 특징 추출 (Time-Frequency Feature Extraction of Broadband Echo Signals from Individual Live Fish for Species Identification)

서지정보

· 발행기관 : 한국수산과학회
· 수록지 정보 : 한국수산과학회지 / 49권 / 2호 / 214 ~ 223페이지
· 저자명 : 이대재, 강희영, 박용예

초록

Joint time-frequency images of the broadband acoustic echoes of six fish species were obtained using the smoothed pseudo-Wigner-Ville distribution (SPWVD). The acoustic features were extracted by changing the sliced window widths and dividing the time window by a 0.02-ms interval and the frequency window by a 20-kHz bandwidth. The 22 spectrum amplitudes obtained in the time and frequency domains of the SPWVD images were fed as input parameters into an artificial neural network (ANN) to verify the effectiveness for species-dependent features related to fish species identification. The results showed that the time-frequency approach improves the extraction of species-specific features for species identification from broadband echoes, compare with time-only or frequency-only features.
The ANN classifier based on these acoustic feature components was correct in approximately 74.5% of the test cases.
In the future, the identification rate will be improved using time-frequency images with reduced dimensions of the broadband acoustic echoes as input for the ANN classifier.

영어초록

Joint time-frequency images of the broadband acoustic echoes of six fish species were obtained using the smoothed pseudo-Wigner-Ville distribution (SPWVD). The acoustic features were extracted by changing the sliced window widths and dividing the time window by a 0.02-ms interval and the frequency window by a 20-kHz bandwidth. The 22 spectrum amplitudes obtained in the time and frequency domains of the SPWVD images were fed as input parameters into an artificial neural network (ANN) to verify the effectiveness for species-dependent features related to fish species identification. The results showed that the time-frequency approach improves the extraction of species-specific features for species identification from broadband echoes, compare with time-only or frequency-only features.
The ANN classifier based on these acoustic feature components was correct in approximately 74.5% of the test cases.
In the future, the identification rate will be improved using time-frequency images with reduced dimensions of the broadband acoustic echoes as input for the ANN classifier.

참고자료

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