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I. Basic Information of the Dataset

  • Dataset Name: Underwater Acoustic Target Radiated Noise Dataset (including the original ShipsEar dataset and the enhanced DS3500 dataset)
  • Dataset Version: V1.0
  • Release Date: July 2025 (based on the paper submission date)
  • Update Records: First release, no updates yet
  • Source and Contributors:
    • Original ShipsEar dataset: Collected along the Atlantic coast of Spain from 2012 to 2013
    • Enhanced DS3500 dataset: Generated by institutions such as the School of Marine Engineering and Technology, Sun Yat - sen University based on ray theory (Contributors: Peng Qian, Jingyi Wang, etc., Affiliations: Sun Yat - sen University, Shanghai Marine Electronic Equipment Research Institute, etc.)
    • Contact information: [email protected]
  • Related Paper: Multi - Task Mixture - of - Experts Model for Underwater Target Localization and Recognition (DOI: 10.3390/1010000)

II. Description of Dataset Content

1. Data Scale and Distribution

Dataset Categories Number of Samples (5 - second segments) Category Distribution Data Format
ShipsEar (original) A, B, C, D, E (including environmental noise) numbered 0 - 4 in sequence 1948 (A:345/B:235/C:785/D:395/E:188) Class A accounts for 17.7%, Class B for 12.1%, Class C for 40.3%, Class D for 20.3%, and Class E (environmental noise) for 9.6% WAV audio (sampling frequency 16kHz)
DS3500 (enhanced) Same as ShipsEar (A - E, numbered 0 - 4 in sequence) 1948 (consistent with the original dataset size) Same as the original dataset WAV audio (sampling frequency 16kHz)

2. Data Sources and Scenarios

  • ShipsEar (original): Actually collected ship radiated noise, covering 11 types of ships (such as motorboats, fishing boats, tugboats, etc.), with a sampling frequency of 52734Hz, preprocessed and segmented into 5 - second segments.
  • DS3500 (enhanced): Synthetic data of deep - sea environment (3500 - meter water depth) generated based on ray theory and BELLHOP sound field model. The simulation scenarios are as follows:
    • Geographical location: Deep - sea area north of the Zhongsha Islands (17.17°N, 114.22°E)
    • Target parameters: Distance from the sonar is 1 - 11km (interval 2km), depth is 100 - 1100m (interval 200m), with a total of 36 simulated positions
    • Sound field environment: The sound speed profile is calculated based on the temperature data from the WOA18 World Ocean Database. The seabed parameters are: sound speed 1601.9m/s, density 1.7g/cm³, attenuation coefficient 0.39f¹·⁷¹ dB/m (f is frequency in kHz)

III. Data Preprocessing and Augmentation

  • ShipsEar preprocessing:
    • Remove blank segments and split into 5 - second short segments to expand the data volume
    • No additional denoising is performed (retaining original noise characteristics)
  • DS3500 augmentation method:
    • Simulate the marine acoustic channel based on ray theory and BELLHOP model
    • Perform channel transmission simulation on the 5 - second segments of ShipsEar to generate received signals including direct and shadow zones
    • Maintain the same sample size as the original dataset (to avoid a decrease in training efficiency)

IV. Data Annotation Information

1. Annotation Content

  • Core labels: Including classification labels, distance labels, and depth labels, as follows:
    • Classification labels: Corresponding to the 5 categories (A - E) of the original ShipsEar (represented by numbers such as "0" in the example for category encoding)
    • Distance labels: Horizontal distance between the target and the sonar (1.000 - 11.000km, accurate to 0.001km)
    • Depth labels: Deployment depth of the sonar (0.100 - 1.100km, accurate to 0.001km)

2. Annotation Example (File Path and Label Format)

train_list.txt

Path                                                                                                    Classification        Distance(km)        Depth(km)
E:\MTQP\wjy_codes\shipsear_5s_16k_ocnwav_Pos\0_0_2.wav                                               	0	       3.000	       0.100
E:\MTQP\wjy_codes\shipsear_5s_16k_ocnwav_Pos\0_0_3.wav                                               	0	       5.000	       0.100

V. Dataset Uses and Applicable Scenarios

  • Main uses:
    • Training and evaluation of underwater acoustic target recognition models
    • Development of underwater target localization (distance, depth) models
    • Verification of multi - task learning (simultaneously achieving recognition and localization) algorithms
  • Applicable scenarios:
    • Marine monitoring in deep - sea and open - sea environments
    • Underwater defense and target detection
    • Performance testing of multi - task learning frameworks (such as MEG) (In the paper, the MEG framework achieved 95.93% recognition accuracy, 0.2011km distance error, and 20.61m depth error on this dataset)

VI. Dataset Usage and Evaluation

  • Data partitioning: A 5 - fold cross - validation strategy (sequential sampling) is adopted. For each type of data, 1 out of every 4 samples is selected as the test set, and the process is repeated 5 times (starting with samples 1 - 5 respectively) to ensure the balance of data order and category distribution.
  • Applicable models: Supports deep learning models (such as CNN, Transformer) and multi - task frameworks (such as MoE, MEG), and is especially suitable for underwater multi - task models that need to integrate position information.

VII. License Agreement

It follows the Creative Commons Attribution (CC BY) license agreement, allowing commercial use, modification, and distribution, with the need to indicate the original author and source.

VIII. Related Resources

  • Related paper: Multi - Task Mixture - of - Experts Model for Underwater Target Localization and Recognition (Authors: Peng Qian et al., Sun Yat - sen University)
  • Recommended model: MEG (multi - task, multi - expert, multi - gate) framework (suitable for target recognition and localization tasks of this dataset)
  • Community support: Technical support can be obtained by contacting the author ([email protected])

IX. Dataset Download and Update

  • Download address: ModelScope Dataset Repository (Search for "Deep - sea Direct Zone - Acoustic Shadow Zone DS3500 Ship Radiated Noise Dataset (DS3500)")
  • Update plan: There is no clear update plan yet. If there is a new version, simulated data of different deep - sea environments (such as different water depths and sea conditions) will be added.
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