| --- |
| license: cc-by-nc-nd-4.0 |
| task_categories: |
| - audio-classification |
| - automatic-speech-recognition |
| - voice-activity-detection |
| tags: |
| - audio |
| pretty_name: DEAR |
| size_categories: |
| - 1K<n<10K |
| configs: |
| - config_name: default |
| data_files: |
| - split: development |
| path: "development.csv" |
| - split: test |
| path: "test.csv" |
| --- |
| |
| # DEAR Dataset |
|
|
| ## Dataset Summary |
|
|
| 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. |
| 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. |
| DEAR enables controlled evaluation of: |
|
|
| * **Context** (environment type: domestic, leisure, nature, professional, transport; indoor/outdoor; stationary/transient noise) |
| * **Speech sources** (speech presence detection; speaker count) |
| * **Acoustic properties** (direct-to-reverberant ratio DRR, reverberation time RT60, signal‑to‑noise ratio SNR) |
|
|
| 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. |
|
|
| ## Tasks |
|
|
| | Task Group | Task | Type | Metric | |
| | ------------- | ----------------------------------- | ----------- | ----------- | |
| | Context | 5‑way environment classification | Multi‑class | Matthews' $\phi$ | |
| | | Indoor vs. outdoor | Binary | Matthews' $\phi$ | |
| | | Stationary vs. transient noise | Binary | Matthews' $\phi$ | |
| | Sources | Speech presence (1 s segments) | Binary | Matthews' $\phi$ | |
| | | Speaker count (1 s segments) | Regression | $R^2$ | |
| | Acoustics | DRR (1 s segments, 1 speaker) | Regression | $R^2$ | |
| | | RT60 (1 s segments, 1 speaker) | Regression | $R^2$ | |
| | | SNR (1 s segments, 1 speaker) | Regression | $R^2$ | |
| | Retrospective | TUT2017 acoustic scene (15 classes) | Multi‑class | Matthews' $\phi$ | |
| | | LibriCount speaker count (0–10) | Regression | $R^2$ | |
|
|
| ## Dataset Structure |
|
|
| ``` |
| ├── data/ |
| │ ├── 00094903-4dbf-44a9-bf09-698fc361dbff.wav |
| │ └── … |
| ├── development.csv |
| └── test.csv |
| ``` |
|
|
| * **.wav files**: mono, 44.1 kHz, 32‑bit float |
| * **.csv files**: meta-data for all tasks, linkable to wav files with `id` |
|
|
| ## Usage |
|
|
| Visit the dedicated code repository: https://github.com/DEAR-dataset/code |
|
|
| ## Source Data |
|
|
| * Speech monologues (proprietary anechoic recordings) |
| * HOA‑SSR library ambisonics scenes (licensed via FORCE Technology) |
| * Impulse responses for controlled reverberation |
|
|
| ## Citation |
|
|
| If you use DEAR in your research, please cite: |
|
|
| ```bibtex |
| @inproceedings{ |
| groeger2025dear, |
| author={Gröger, Fabian and Baumann, Pascal and Amruthalingam, Ludovic and Simon, Laurent and Giurda, Ruksana and Lionetti, Simone}, |
| booktitle={ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, |
| title={Evaluation of Deep Audio Representations for Hearables}, |
| year={2025}, |
| doi={10.1109/ICASSP49660.2025.10887737} |
| } |
| ``` |
|
|
| ArXiv version: arxiv.org/abs/2502.06664 |
