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--- |
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license: cc-by-nc-nd-4.0 |
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task_categories: |
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- audio-classification |
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- automatic-speech-recognition |
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- voice-activity-detection |
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tags: |
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- audio |
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pretty_name: DEAR |
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size_categories: |
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- 1K<n<10K |
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configs: |
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- config_name: default |
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data_files: |
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- split: development |
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path: "development.csv" |
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- split: test |
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path: "test.csv" |
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--- |
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# DEAR Dataset |
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## Dataset Summary |
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The Deep Evaluation of Audio Representations (DEAR) dataset is a benchmark designed to assess general-purpose audio foundation models on properties critical for hearable devices. |
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It comprises **1,158** mono audio tracks (30 s each), spatially mixing proprietary anechoic speech monologues with high-quality everyday acoustic scene recordings from the HOA‑SSR library. |
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DEAR enables controlled evaluation of: |
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* **Context** (environment type: domestic, leisure, nature, professional, transport; indoor/outdoor; stationary/transient noise) |
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* **Speech sources** (speech presence detection; speaker count) |
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* **Acoustic properties** (direct-to-reverberant ratio DRR, reverberation time RT60, signal‑to‑noise ratio SNR) |
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All tracks are down‑mixed to a single channel at 44.1 kHz (32‑bit) and split into development and test sets with no overlap in speakers, backgrounds, or impulse responses. |
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## Tasks |
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| Task Group | Task | Type | Metric | |
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| ------------- | ----------------------------------- | ----------- | ----------- | |
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| Context | 5‑way environment classification | Multi‑class | Matthews' $\phi$ | |
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| | Indoor vs. outdoor | Binary | Matthews' $\phi$ | |
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| | Stationary vs. transient noise | Binary | Matthews' $\phi$ | |
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| Sources | Speech presence (1 s segments) | Binary | Matthews' $\phi$ | |
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| | Speaker count (1 s segments) | Regression | $R^2$ | |
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| Acoustics | DRR (1 s segments, 1 speaker) | Regression | $R^2$ | |
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| | RT60 (1 s segments, 1 speaker) | Regression | $R^2$ | |
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| | SNR (1 s segments, 1 speaker) | Regression | $R^2$ | |
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| Retrospective | TUT2017 acoustic scene (15 classes) | Multi‑class | Matthews' $\phi$ | |
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| | LibriCount speaker count (0–10) | Regression | $R^2$ | |
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## Dataset Structure |
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``` |
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├── data/ |
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│ ├── 00094903-4dbf-44a9-bf09-698fc361dbff.wav |
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│ └── … |
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├── development.csv |
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└── test.csv |
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``` |
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* **.wav files**: mono, 44.1 kHz, 32‑bit float |
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* **.csv files**: meta-data for all tasks, linkable to wav files with `id` |
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## Usage |
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Visit the dedicated code repository: https://github.com/DEAR-dataset/code |
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## Source Data |
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* Speech monologues (proprietary anechoic recordings) |
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* HOA‑SSR library ambisonics scenes (licensed via FORCE Technology) |
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* Impulse responses for controlled reverberation |
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## Citation |
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If you use DEAR in your research, please cite: |
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```bibtex |
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@inproceedings{ |
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groeger2025dear, |
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author={Gröger, Fabian and Baumann, Pascal and Amruthalingam, Ludovic and Simon, Laurent and Giurda, Ruksana and Lionetti, Simone}, |
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booktitle={ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
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title={Evaluation of Deep Audio Representations for Hearables}, |
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year={2025}, |
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doi={10.1109/ICASSP49660.2025.10887737} |
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} |
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``` |
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ArXiv version: arxiv.org/abs/2502.06664 |
