README.md

---
language:
- en
license: apache-2.0
size_categories:
- 10M<n<100M
task_categories:
- audio-to-audio
pretty_name: Hive Dataset
arxiv: 2601.22599
tags:
- audio
- sound-separation
- universal-sound-separation
- audio-mixing
- audioset
dataset_info:
  features:
  - name: mix_id
    dtype: string
  - name: split
    dtype: string
  - name: sample_rate
    dtype: int32
  - name: target_duration
    dtype: float64
  - name: num_sources
    dtype: int32
  - name: sources
    sequence:
    - name: source_id
      dtype: string
    - name: path
      dtype: string
    - name: label
      dtype: string
    - name: crop_start_second
      dtype: float64
    - name: crop_end_second
      dtype: float64
    - name: chunk_start_second
      dtype: float64
    - name: chunk_end_second
      dtype: float64
    - name: rms_gain
      dtype: float64
    - name: snr_db
      dtype: float64
    - name: applied_weight
      dtype: float64
  - name: global_normalization_factor
    dtype: float64
  - name: final_max_amplitude
    dtype: float64
  splits:
  - name: train
    num_examples: 5000000
  - name: validation
    num_examples: 500000
  - name: test
    num_examples: 100000
---

<h1 align="center">A Semantically Consistent Dataset for Data-Efficient Query-Based Universal Sound Separation</h1>
<p align="center">
  <img src="asserts/logo.png" alt="Logo" width="250"/>
</p>
<p align="center">
  <strong>Kai Li<sup>*</sup>, Jintao Cheng<sup>*</sup>, Chang Zeng, Zijun Yan, Helin Wang, Zixiong Su, Bo Zheng, Xiaolin Hu</strong><br>
    <strong>Tsinghua University, Shanda AI, Johns Hopkins University</strong><br>
    <strong><sup>*</sup>Equal contribution</strong><br>
    <strong>Completed during Kai Li's internship at Shanda AI.</strong><br>
  <a href="https://arxiv.org/abs/2601.22599">📜 Arxiv 2026</a> | <a href="https://github.com/ShandaAI/Hive">💻 Code</a> | <a href="https://shandaai.github.io/Hive/">🎶 Demo</a>
</p>

## Usage

```python
from datasets import load_dataset

# Load full dataset
dataset = load_dataset("ShandaAI/Hive")

# Load specific split
train_data = load_dataset("ShandaAI/Hive", split="train")

# Streaming mode (recommended for large datasets)
dataset = load_dataset("ShandaAI/Hive", streaming=True)
```

## 📄 Dataset Description

**Hive** is a high-quality synthetic dataset designed for **Universal Sound Separation (USS)**. Unlike traditional methods relying on weakly-labeled in-the-wild data, Hive leverages an automated data collection pipeline to mine high-purity single-event segments from complex acoustic environments and synthesizes mixtures with semantically consistent constraints.

### Key Features

- **Purity over Scale**: 2.4k hours achieving competitive performance with million-hour baselines (~0.2% data scale)
- **Single-label Clean Supervision**: Rigorous semantic-acoustic alignment eliminating co-occurrence noise
- **Semantically Consistent Mixing**: Logic-based co-occurrence matrix ensuring realistic acoustic scenes
- **High Fidelity**: 44.1kHz sample rate for high-quality audio

### Dataset Scale

| Metric | Value |
|--------|-------|
| **Training Set Raw Audio** | 2,442 hours |
| **Val & Test Set Raw Audio** | 292 hours |
| **Mixed Samples** | 19.6M mixtures |
| **Total Mixed Duration** | ~22.4k hours |
| **Label Categories** | 283 classes |
| **Sample Rate** | 44.1 kHz |
| **Training Sample Duration** | 4 seconds |
| **Test Sample Duration** | 10 seconds |

### Dataset Splits

| Split | Samples | Description |
|-------|---------|-------------|
| Train | 17.5M | Training mixtures (4s duration) |
| Validation | 1.75M | Validation mixtures |
| Test | 350k | Test mixtures (10s duration) |

---

## 📂 Dataset Structure

### Directory Organization

```
hive-datasets-parquet/
├── README.md
├── train/
│   └── data.parquet
├── validation/
│   └── data.parquet
└── test/
    └── data.parquet
```

Each split contains a single Parquet file with all mixture metadata. The `num_sources` field indicates the number of sources (2-5) for each mixture.

---

## 📋 Data Fields

### JSON Schema

Each JSON object contains complete generation parameters for reproducing a mixture sample:

```python
{
    "mix_id": "sample_00000003",
    "split": "train",
    "sample_rate": 44100,
    "target_duration": 4.0,
    "num_sources": 2,
    "sources": {
        "source_id": ["s1", "s2"],
        "path": ["relative/path/to/audio1", "relative/path/to/audio2"],
        "label": ["Ocean", "Rain"],
        "crop_start_second": [1.396, 2.5],
        "crop_end_second": [5.396, 6.5],
        "chunk_start_second": [35.0, 20.0],
        "chunk_end_second": [45.0, 30.0],
        "rms_gain": [3.546, 2.1],
        "snr_db": [0.0, -3.0],
        "applied_weight": [3.546, 1.487]
    },
    "global_normalization_factor": 0.786,
    "final_max_amplitude": 0.95
}
```

### Field Descriptions

#### 1. Basic Info Fields

| Field | Type | Description |
|-------|------|-------------|
| `mix_id` | string | Unique identifier for the mixture task |
| `split` | string | Dataset partition (`train` / `validation` / `test`) |
| `sample_rate` | int32 | Audio sample rate in Hz (44100) |
| `target_duration` | float64 | Target duration in seconds (4.0 for train, 10.0 for test) |
| `num_sources` | int32 | Number of audio sources in this mixture (2-5) |

#### 2. Source Information (`sources`)

Metadata required to reproduce the mixing process for each audio source. Stored in columnar format (dict of lists) for efficient Parquet storage:

| Field | Type | Description |
|-------|------|-------------|
| `source_id` | list[string] | Source identifiers (`s1`, `s2`, ...) |
| `path` | list[string] | Relative paths to the source audio files |
| `label` | list[string] | AudioSet ontology labels for each source |
| `chunk_start_second` | list[float64] | Start times (seconds) for reading from original audio files |
| `chunk_end_second` | list[float64] | End times (seconds) for reading from original audio files |
| `crop_start_second` | list[float64] | Precise start positions (seconds) for reproducible random extraction |
| `crop_end_second` | list[float64] | Precise end positions (seconds) for reproducible random extraction |
| `rms_gain` | list[float64] | Energy normalization coefficients: $\text{target\_rms} / \text{current\_rms}$ |
| `snr_db` | list[float64] | Signal-to-noise ratios in dB assigned to each source |
| `applied_weight` | list[float64] | Final scaling weights: $\text{rms\_gain} \times 10^{(\text{snr\_db} / 20)}$ |

#### 3. Mixing Parameters

Global processing parameters after combining multiple audio sources:

| Field | Type | Description |
|-------|------|-------------|
| `global_normalization_factor` | float64 | Anti-clipping scaling coefficient: $0.95 / \text{max\_val}$ |
| `final_max_amplitude` | float64 | Maximum amplitude threshold (0.95) to prevent bit-depth overflow |

### Detailed Field Explanations

#### Cropping Logic
- `chunk_start/end_second`: Defines the reading interval from the original audio file
- `crop_start/end_second`: Records the precise random cropping position, ensuring exact reproducibility across runs

#### Energy Normalization (`rms_gain`)
Adjusts different audio sources to the same energy level:
$$\text{rms\_gain} = \frac{\text{target\_rms}}{\text{current\_rms}}$$

#### Signal-to-Noise Ratio (`snr_db`)
The SNR value assigned to each source, sampled from a predefined range using `random.uniform(snr_range[0], snr_range[1])`.

#### Applied Weight
The comprehensive scaling weight combining energy normalization and SNR adjustment:
$$\text{applied\_weight} = \text{rms\_gain} \times 10^{(\text{snr\_db} / 20)}$$

This is the final coefficient applied to the original waveform.

#### Global Normalization Factor
Prevents audio clipping after mixing:
$$\text{global\_normalization\_factor} = \frac{0.95}{\text{max\_val}}$$

Where `max_val` is the **peak amplitude (absolute value)** of the mixed signal.

---

## 🔧 Usage

### Download Metadata

```python
from datasets import load_dataset

# Load specific split and mixture type
dataset = load_dataset("ShandaAI/Hive", split="train")
```

### Generate Mixed Audio

Please refer to the [official GitHub repository](https://github.com/ShandaAI/Hive) for the complete audio generation pipeline.

```bash
# Clone the repository
git clone https://github.com/ShandaAI/Hive.git
cd Hive/hive_dataset

# Generate mixtures from metadata
python mix_from_metadata/mix_from_metadata.py \
    --metadata_dir /path/to/downloaded/metadata \
    --output_dir ./hive_dataset \
    --dataset_paths dataset_paths.json \
    --num_processes 16
```

---

## 📚 Source Datasets

Hive integrates **12 public datasets** to construct a long-tailed acoustic space:

| # | Dataset | Clips | Duration (h) | License |
|---|---------|-------|--------------|---------|
| 1 | BBC Sound Effects | 369,603 | 1,020.62 | Remix License |
| 2 | AudioSet | 326,890 | 896.61 | CC BY |
| 3 | VGGSound | 115,191 | 319.10 | CC BY 4.0 |
| 4 | MUSIC21 | 32,701 | 90.28 | YouTube Standard |
| 5 | FreeSound | 17,451 | 46.90 | CC0/BY/BY-NC |
| 6 | ClothoV2 | 14,759 | 38.19 | Non-Commercial Research |
| 7 | Voicebank-DEMAND | 12,376 | 9.94 | CC BY 4.0 |
| 8 | AVE | 3,054 | 6.91 | CC BY-NC-SA |
| 9 | SoundBible | 2,501 | 5.78 | CC BY 4.0 |
| 10 | DCASE | 1,969 | 5.46 | Academic Use |
| 11 | ESC50 | 1,433 | 1.99 | CC BY-NC 3.0 |
| 12 | FSD50K | 636 | 0.80 | Creative Commons |
| | **Total** | **898,564** | **2,442.60** | |

**Important Note**: This repository releases only **metadata** (JSON files containing mixing parameters and source references) for reproducibility. Users must independently download and prepare the source datasets according to their respective licenses.

---

## 📖 Citation

If you use this dataset, please cite:

```bibtex
@article{li2026hive,
  title={A Semantically Consistent Dataset for Data-Efficient Query-Based Universal Sound Separation},
  author={Li, Kai and Cheng, Jintao and Zeng, Chang and Yan, Zijun and Wang, Helin and Su, Zixiong and Zheng, Bo and Hu, Xiaolin},
  journal={arXiv preprint arXiv:2601.22599},
  year={2026}
}
```

---

## ⚖️ License

This dataset metadata is released under the **Apache License 2.0**.

Please note that the source audio files are subject to their original licenses. Users must comply with the respective licenses when using the source datasets.

---

## 🙏 Acknowledgments

We extend our gratitude to the researchers and organizations who curated the foundational datasets that made Hive possible:

- **BBC Sound Effects** - Professional-grade recordings with broadcast-level fidelity
- **AudioSet** (Google) - Large-scale audio benchmark
- **VGGSound** (University of Oxford) - Real-world acoustic diversity
- **FreeSound** (MTG-UPF) - Rich crowdsourced soundscapes
- And all other contributing datasets

---

## 📬 Contact

For questions or issues, please open an issue on the [GitHub repository](https://github.com/ShandaAI/Hive) or contact the authors.