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README.md

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----
-license: mit
----
+---
+title: DCASE 5-Class 3-Source Separation 32k
+license: mit
+tags:
+- audio
+- dcase
+- audio-source-separation
+- 32k
+- dcase-derived
+language:
+- en
+task_categories:
+- audio-to-audio
+size_categories:
+- 10K<n<100K
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path:
+    - metadata/train_metadata.jsonl
+    - mixtures/train/*
+    - noise/train/*
+    - sound_event/train/*
+  - split: valid
+    path:
+    - metadata/valid_metadata.jsonl
+    - mixtures/valid/*
+    - noise/valid/*
+    - sound_event/valid/*
+  - split: test
+    path:
+    - metadata/test_metadata.jsonl
+    - mixtures/test/*
+    - noise/test/*
+    - sound_event/test/*
+---
+
+# DCASE 5-Class 3-Source Separation 32k
+
+## Dataset Description
+
+This dataset is a collection of 10,000 synthetic audio mixtures designed for the task of **audio source separation**.
+
+Each audio file is a 10-second, **32kHz** mixture containing **3 distinct audio sources** from a pool of **5 selected classes**. The mixtures were generated with a random Signal-to-Noise Ratio (SNR) between 5 and 20 dB.
+
+This dataset is ideal for training and evaluating models that aim to separate a mixed audio signal into its constituent sources.
+
+The 5 selected source classes are:
+* Speech
+* FootSteps
+* Doorbell
+* Dishes
+* AlarmClock
+
+## Dataset Generation
+
+The dataset was generated using the following Python configuration. This provides a 100% reproducible recipe for the data.
+
+```python
+SELECTED_CLASSES = [
+  "Speech",
+  "FootSteps",
+  "Doorbell",
+  "Dishes",
+  "AlarmClock"
+]
+
+N_MIXTURES = 10_000
+N_SOURCES = 3
+DURATION = 10.0
+SR = 32000
+SNR_RANGE = [5, 20]
+TARGET_PEAK = 0.95
+MIN_GAIN = 3.0
+
+SPLIT_DATA = {
+  'train': {
+    'source_event_dir': 'test/oracle_target',
+    'source_noise_dir': 'noise/train',
+    'split_noise': False,
+    'portion': 0.70
+  },
+  'valid': {
+    'source_event_dir': 'sound_event/train',
+    'source_noise_dir': 'noise/valid',
+    'split_noise': True,
+    'noise_portion': 0.50,
+    'portion': 0.15
+  },
+  'test': {
+    'source_event_dirs': ['test/oracle_target', 'sound_event/valid'],
+    'source_noise_dir': 'noise/valid',
+    'split_noise': True,
+    'noise_portion': 0.50,
+    'portion': 0.15
+  }
+}
+```
+
+## Data Splits
+
+The dataset is split into `train`, `valid`, and `test` sets as defined in the generation config.
+
+| Split | Portion | Number of Mixtures |
+| :--- | :--- | :--- |
+| `train` | 70% | 7,000 |
+| `valid` | 15% | 1,500 |
+| `test` | 15% | 1,500 |
+| **Total** | **100%** | **10,000** |
+
+## Data Fields
+
+This dataset is built on a central metadata file (metadata/mixtures_metadata.json) which contains an entry for each generated mixture.
+
+A single entry in the metadata has the following structure:
+
+```json
+{
+  "mixture_id": "mixture_000001",
+  "mixture_path": "mixtures/train/mixture_000001.wav",
+  "split": "train",
+  "config": {
+    "duration": 10.0,
+    "sr": 32000,
+    "max_event_overlap": 3,
+    "ref_channel": 0
+  },
+  "fg_events": [
+    {
+      "label": "Speech",
+      "source_file": "dcase_source_files/speech_001.wav",
+      "source_time": 0.0,
+      "event_time": 1.234567,
+      "event_duration": 2.500000,
+      "snr": 15.678901,
+      "role": "foreground"
+    },
+    {
+      "label": "Doorbell",
+      "source_file": "dcase_source_files/doorbell_002.wav",
+      "source_time": 0.0,
+      "event_time": 4.500000,
+      "event_duration": 1.800000,
+      "snr": 10.123456,
+      "role": "foreground"
+    }
+  ],
+  "bg_events": [
+    {
+      "label": null,
+      "source_file": "dcase_noise_files/ambient_noise_001.wav",
+      "source_time": 0.0,
+      "event_time": 0.0,
+      "event_duration": 10.0,
+      "snr": 0.0,
+      "role": "background"
+    }
+  ],
+  "int_events": [],
+  "normalization_gain": 0.85,
+  "original_peak": 1.123
+}
+```
+
+### Field Descriptions
+
+  * **`mixture_id`**: A unique identifier for the mixture.
+  * **`mixture_path`**: The relative path to the generated mixture `.wav` file.
+  * **`split`**: The data split this mixture belongs to (`train`, `valid`, or `test`).
+  * **`config`**: An object containing the main generation parameters for this file.
+  * **`fg_events`**: A list of "foreground" sound event objects. Each object contains:
+      * **`label`**: The class of the event (e.g., "Speech", "Doorbell").
+      * **`source_file`**: The relative path to the original clean audio file used.
+      * **`event_time`**: The onset time (in seconds) of the event in the mixture.
+      * **`event_duration`**: The duration (in seconds) of the event.
+      * **`snr`**: The target Signal-to-Noise Ratio (in dB) of this event against the background.
+      * **`role`**: Always "foreground".
+  * **`bg_events`**: A list of "background" noise objects (usually one). It has the same structure as `fg_events`, but the `label` is `null` and `snr` is `0.0`.
+  * **`int_events`**: A list for "interfering" events (unused in this config, so it's `[]`).
+  * **`normalization_gain`**: The gain (e.g., `0.85`) applied to the final mixture to reach the `TARGET_PEAK`.
+  * **`original_peak`**: The peak amplitude of the mixture *before* normalization.
+## Intended Use
+
+This dataset is primarily intended for training and evaluating audio source separation models, particularly those that can handle:
+
+  * 3-source separation
+  * 32kHz sampling rate
+  * SNRs in the 5-20 dB range
+
+## Citation
+
+### Citing the Original DCASE Data
+
+```bibtex
+@dataset{yasuda_masahiro_2025_15117227,
+  author       = {Yasuda, Masahiro and
+                  Nguyen, Binh Thien and
+                  Harada, Noboru and
+                  Takeuchi, Daiki},
+  title        = {{DCASE2025Task4Dataset: The Dataset for Spatial 
+                   Semantic Segmentation of Sound Scenes}},
+  month        = apr,
+  year         = 2025,
+  publisher    = {Zenodo},
+  version      = {1.0.0},
+  doi          = {10.5281/zenodo.15117227},
+  url          = {https://doi.org/10.5281/zenodo.15117227}
+}
+```
+
+### Citing this Dataset
+
+If you use this specific dataset generation recipe, please cite it as:
+
+```bibtex
+@misc/Kiuyha2025dcase5class3source,
+  title  = {DCASE 3-Source Separation 32k Dataset},
+  author = {[Kiuyha]},
+  year   = {2025},
+  url    = {https://huggingface.co/datasets/Kiuyha/dcase-5class-3source-mixtures-32k},
+  howpublished = {Hugging Face Datasets}
+}
+```
+
+## License
+
+The original DCASE source data has its own license. Please refer to the official DCASE website for details.
+
+This derived dataset (the mixture 'recipe' and generated files) is made available under the [MIT LICENCE](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/mit.md).