--- license: cc-by-4.0 tasks: - underwater_acoustic_target_recognition # Corresponding to the underwater acoustic target recognition task - underwater_target_localization # Corresponding to the underwater target localization task - multi_task_learning # Corresponding to multi - task learning scenarios frameworks: # Supported deep learning frameworks, not explicitly mentioned in the document, here are general examples - pytorch - tensorflow language: # The dataset is audio data, with no specific language, left blank or marked as none here - en tags: - underwater_acoustics - data_augmentation - ray_theory datasets: train: - ShipsEar - DS3500 test: - ShipsEar - DS3500 evaluation: - ShipsEar - DS3500 metrics: - accuracy - range_localization_error - depth_localization_error base_model: - MEG (multi - task, multi - expert, multi - gate) framework indexing: results: - task: name: Underwater Target Recognition dataset: name: DS3500 type: audio args: default metrics: - type: accuracy value: 95.93% description: recognition accuracy on DS3500 dataset args: default - task: name: Underwater Target Localization dataset: name: DS3500 type: audio args: default metrics: - type: range_localization_error value: 0.2011 km description: range localization error on DS3500 dataset args: default - type: depth_localization_error value: 20.61 m description: depth localization error on DS3500 dataset args: default domain: - audio # The dataset is of audio type, belonging to the acoustic field --- - [English](README.md) - [中文](README-zh.md) ## 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: qianp@mail2.sysu.edu.cn - **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 (qianp@mail2.sysu.edu.cn) ## IX. Dataset Download and Update - Download address: [ModelScope Dataset Repository](https://modelscope.cn/datasets) (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.