{
  "abstract": "Underwater acoustic signals (UAS) in marine environments are often contaminated by noise from biological activity, ocean waves, and equipment. Under low signal-to-noise ratio (SNR) conditions, target ship-radiated components and background noise are strongly mixed, making reliable underwater detection, recognition, and communication challenging. To address this problem, this paper proposes Multi-Scale TCN, DPRNN, and Attention TasNet (MSDA-TasNet), a lightweight end-to-end source separation network for underwater ship-radiated noise. The proposed method combines time-domain convolutional encoding, a dual-path recurrent neural network (DPRNN), and a multi-scale temporal convolutional network (multi-scale TCN) to model both short-term details and long-term dependencies of UAS. To further enhance target-aware feature representation, this paper designs a novel channel-frequency attention mechanism that adaptively emphasizes target-related spectral cues. Experiments on two underwater acoustic datasets show that MSDA-TasNet achieves better separation performance than several representative baselines in terms of Signal-to-Distortion Ratio (SDR), Scale-Invariant Signal-to-Distortion Ratio (SI-SDR), SDR improvement (SDRi), SI-SDR improvement (SI-SDRi), and Segmental Signal-to-Distortion Ratio (SegSDR). The results indicate that the proposed network can improve target signal recovery under complex underwater acoustic noise while maintaining a relatively low parameter count and computational cost.",
  "abstract_shown": true,
  "affiliations": [
    "School of Computer Science, Xi'an Polytechnic University, Xi'an 710600, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Junxian Dong"
    },
    {
      "affs": [
        0
      ],
      "name": "Qiong Yang",
      "orcid": "0000-0002-0601-5073"
    },
    {
      "affs": [
        0
      ],
      "name": "Ying Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/77z8fkytseda3.bib",
  "cited_by": 0,
  "doi": "10.3390/s26206382",
  "fulltext_url": "https://europepmc.org/articles/PMC13709171",
  "id": "77z8fkytseda3",
  "issns": [
    "1424-8220"
  ],
  "landing_url": "https://europepmc.org/article/MED/42944242",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1711|Speech and Audio Processing"
    ]
  ],
  "openalex": "W7221163871",
  "pdf_url": "https://europepmc.org/articles/PMC13709171?pdf=render",
  "pmcid": "PMC13709171",
  "pmid": "42944242",
  "published": "2026-10-09",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/77z8fkytseda3.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:24Z",
      "native_id": "MED:42944242",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42944242"
    }
  ],
  "subjects": [],
  "title": "A Lightweight Channel-Frequency Joint Attention Network for Underwater Ship-Radiated Noise Separation.",
  "title_text": "A Lightweight Channel-Frequency Joint Attention Network for Underwater Ship-Radiated Noise Separation.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:22:24Z",
  "url": "https://research.draconitic.com/paper/77z8fkytseda3",
  "venue": "Sensors (Basel, Switzerland)",
  "versions": []
}