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DS3500.zip

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README-zh.md

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+---
+license: Creative Commons Attribution (CC BY) 4.0 # 开源许可证类型，依据文档中数据集遵循的许可协议
+tasks:
+  - underwater_acoustic_target_recognition # 对应水下声学目标识别任务
+  - underwater_target_localization # 对应水下目标定位任务
+  - multi_task_learning # 对应多任务学习场景
+frameworks: # 支持的深度学习框架，文档未明确提及，此处为通用示例
+  - pytorch
+  - tensorflow
+language: # 数据集为音频数据，无特定语言，此处留空或标注为无
+  - none
+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 # 数据集为音频类型，属于声学领域
+---
+
+- [英文](README.md)
+- [中文](README-zh.md)
+
+## 一、数据集基础信息
+- **数据集名称**：水下声学目标辐射噪声数据集（含原始ShipsEar数据集及增强DS3500数据集）
+- **数据集版本**：V1.0
+- **发布时间**：2025年7月（基于论文提交时间）
+- **更新记录**：首次发布，暂无更新
+- **来源与贡献者**：
+  - 原始ShipsEar数据集：采集于2012-2013年西班牙大西洋沿岸
+  - 增强DS3500数据集：由中山大学海洋工程与技术学院等机构基于射线理论生成（贡献者：Peng Qian、Jingyi Wang等，单位：中山大学、上海船舶电子设备研究所等）
+  - 通讯方式：qianp@mail2.sysu.edu.cn
+- **关联论文**：*Multi-Task Mixture-of-Experts Model for Underwater Target Localization and Recognition*（DOI: 10.3390/1010000）
+
+
+## 二、数据集内容描述
+### 1. 数据规模与分布
+| 数据集 | 类别 | 样本数量（5秒片段） | 类别分布 | 数据格式 |
+|--------|------|---------------------|----------|----------|
+| ShipsEar（原始） | A、B、C、D、E（含环境噪声） 数字顺序0-4| 1948（A:345/B:235/C:785/D:395/E:188） | A类占17.7%、B类占12.1%、C类占40.3%、D类占20.3%、E类（环境噪声）占9.6% | WAV音频（采样频率16kHz） |
+| DS3500（增强） | 同ShipsEar（A-E，数字顺序0-4） | 1948（与原始数据集规模一致） | 同原始数据集 | WAV音频（采样频率16kHz） |
+
+
+### 2. 数据来源与场景
+- **ShipsEar（原始）**：实际采集的船舶辐射噪声，涵盖11种船舶类型（如摩托艇、渔船、拖船等），采样频率52734Hz，经预处理后分割为5秒片段。
+- **DS3500（增强）**：基于射线理论和BELLHOP声场模型生成的深海环境（3500米水深）合成数据，模拟场景为：
+  - 地理位置：中沙群岛以北深海区域（17.17°N，114.22°E）
+  - 目标参数：距离声纳1-11km（间隔2km）、深度100-1100m（间隔200m），共36个模拟位置
+  - 声场环境：基于WOA18世界海洋数据库温度数据计算声速剖面，海底参数为声速1601.9m/s、密度1.7g/cm³、衰减系数0.39f¹·⁷¹ dB/m（f为频率，单位kHz）
+
+
+## 三、数据预处理与增强
+- **ShipsEar预处理**：
+  - 去除空白片段，分割为5秒短片段以扩展数据量
+  - 未进行额外去噪（保留原始噪声特性）
+- **DS3500增强方法**：
+  - 基于射线理论和BELLHOP模型模拟海洋声学信道
+  - 对ShipsEar的5秒片段进行信道传输模拟，生成包含直达区和阴影区的接收信号
+  - 保持与原始数据集相同的样本量（避免训练效率下降）
+
+
+## 四、数据标注信息
+### 1. 标注内容
+- **核心标签**：包含分类标签、距离标签、深度标签，具体如下：
+  - 分类标签：对应原始ShipsEar的5个类别（A-E，示例中以数字“0”等表示类别编码）
+  - 距离标签：目标与声纳的水平距离（1.000-11.000km，精确到0.001km）
+  - 深度标签：声纳的部署深度（0.100-1.100km，精确到0.001km）
+
+### 2. 标注示例（文件路径与标签格式）
+train_list.txt
+```
+路径                                                                                                    分类        距离(km)        深度(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
+```
+
+
+
+## 五、数据集用途与适用场景
+- **主要用途**：
+  - 水下声学目标识别模型训练与评估
+  - 水下目标定位（距离、深度）模型开发
+  - 多任务学习（同时实现识别与定位）算法验证
+- **适用场景**：
+  - 深海远海环境下的 marine monitoring（海洋监测）
+  - 水下防御与目标探测
+  - 多任务学习框架（如MEG）的性能测试（论文中MEG框架在本数据集上实现95.93%识别准确率、0.2011km距离误差、20.61m深度误差）
+
+
+## 六、数据集使用与评估
+- **数据划分**：采用5折交叉验证策略（顺序抽样），每类数据中每4个样本选1个作为测试集，迭代5次（分别以1-5号样本为起点），确保数据顺序、类别分布平衡。
+- **适用模型**：支持深度学习模型（如CNN、Transformer）及多任务框架（如MoE、MEG），尤其适合需要融合位置信息的水下多任务模型。
+
+
+## 七、许可协议
+遵循Creative Commons Attribution（CC BY）许可协议，允许商用、修改、分发，需注明原作者及来源。
+
+
+## 八、相关资源
+- 关联论文：*Multi-Task Mixture-of-Experts Model for Underwater Target Localization and Recognition*（作者：Peng Qian等，中山大学）
+- 推荐模型：MEG（multi-task, multi-expert, multi-gate）框架（适用于本数据集的目标识别与定位任务）
+- 社区支持：可联系作者（qianp@mail2.sysu.edu.cn）获取技术支持
+
+
+## 九、数据集下载与更新
+- 下载地址：[魔搭社区数据集仓库](https://modelscope.cn/datasets)（搜索“深海直达区-声影区DS3500船舶辐射噪声数据集（DS3500）”）
+- 更新计划：暂无明确更新计划，若有新版本将补充不同深海环境（如不同水深、海况）的模拟数据。

README.md

@@ -1,3 +1,153 @@
 ---
-license: mit
+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.

ShipsEar.zip

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