Deep cepstrum-wavelet autoencoder: A novel intelligent sonar classifier
作者：
Highlights：
• A novel deep wavelet autoencoder is proposed.
• Cepstral lifters reduce the multi-path distortion and time-varying effects.
• Wavelet autoencoder is used to extract fluctuating frequency signature of vessels.
• Three underwater acoustic datasets are exploited.
• The results are evaluated, comparing ten state-of-the-art methods.
摘要
•A novel deep wavelet autoencoder is proposed.•Cepstral lifters reduce the multi-path distortion and time-varying effects.•Wavelet autoencoder is used to extract fluctuating frequency signature of vessels.•Three underwater acoustic datasets are exploited.•The results are evaluated, comparing ten state-of-the-art methods.
论文关键词：Underwater target classification,Deep cepstral liftering,Autoencoders,Wavelet network
论文评审过程：Received 6 August 2021, Revised 13 March 2022, Accepted 22 April 2022, Available online 28 April 2022, Version of Record 6 May 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.117295
