| --- |
| license: cc-by-4.0 |
| task_categories: |
| - audio-classification |
| tags: |
| - speech |
| - acoustic-echo-cancellation |
| - aec-challenge |
| - icassp-2022 |
| pretty_name: Microsoft AEC Challenge 16kHz (FLAC) |
| --- |
| |
| # Microsoft AEC Challenge 16kHz |
|
|
| [Microsoft AEC Challenge](https://github.com/microsoft/AEC-Challenge) dataset |
| converted from 16kHz WAV to **FLAC** (lossless compression) and packed into tar shards. |
|
|
| Source: the `datasets/` directory of the microsoft/AEC-Challenge Git LFS repo. |
| Covers all challenge years (2021, ICASSP 2022, ICASSP 2023). |
|
|
| ## Structure |
|
|
| ### Real recordings |
|
|
| Paired loopback (far-end reference) and microphone recordings from real devices. |
|
|
| - `real/` — 37,578 files, single playback real recordings |
| - `real_doubled/` — 10,531 files, double playback real recordings |
|
|
| Filenames preserve the GUID-based naming convention: |
| `{GUID}_{scenario}_{signal}.flac` |
|
|
| Scenarios: `farend_singletalk`, `farend_singletalk_with_movement`, `nearend_singletalk`, |
| `doubletalk`, `doubletalk_with_movement`, `sweep` |
|
|
| Signals: `lpb` (loopback/far-end reference), `mic` (microphone recording) |
|
|
| ### Synthetic data (10,000 clips) |
|
|
| - `synthetic_echo/` — Echo signal component |
| - `synthetic_farend/` — Far-end reference signal |
| - `synthetic_nearend_mic/` — Mixed microphone signal (echo + near-end + noise) |
| - `synthetic_nearend_speech/` — Clean near-end speech |
| - `meta.csv` — Synthetic data metadata |
|
|
| ### Test sets |
|
|
| - `test_set/` — Original test set (clean + noisy) |
| - `test_set_icassp2022/` — ICASSP 2022 test set |
| - `blind_test_set/` — Original blind test set |
| - `blind_test_set_icassp2022/` — ICASSP 2022 blind test set |
| - `blind_test_set_icassp2023/` — ICASSP 2023 blind test set |
| - `blind_test_set_interspeech2021/` — Interspeech 2021 blind test set |
|
|
| ## Usage |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| import tarfile |
| from pathlib import Path |
| |
| # Download |
| local = snapshot_download("richiejp/aec-challenge-16k", local_dir="/data/aec", repo_type="dataset") |
| |
| # Extract all shards |
| for tar_path in sorted(Path(local).rglob("*.tar")): |
| with tarfile.open(tar_path) as tf: |
| tf.extractall(tar_path.parent) |
| ``` |
|
|
| ## Source |
|
|
| Original data from Microsoft's AEC Challenge: |
| - https://github.com/microsoft/AEC-Challenge |
| - License: CC-BY-4.0 (see original repo for details) |
|
|
