README.md

---
dataset_info:
- config_name: seen
  features:
  - name: id
    dtype: string
  - name: device_class
    dtype: string
  - name: array_id
    dtype: string
  - name: condition
    dtype: string
  - name: n_mics
    dtype: int32
  - name: n_samples
    dtype: int64
  - name: sir_db
    dtype: float32
  - name: snr_db
    dtype: float32
  - name: sensor_snr_db
    dtype: float32
  - name: rt60
    dtype: float32
  - name: drr_db
    dtype: float32
  - name: mic_positions
    sequence:
      sequence: float32
  - name: mix
    dtype:
      audio:
        sampling_rate: 48000
  - name: target
    dtype:
      audio:
        sampling_rate: 48000
  - name: enroll
    dtype:
      audio:
        sampling_rate: 48000
  splits:
  - name: train
    num_bytes: 23028272020.0
    num_examples: 10000
  - name: dev
    num_bytes: 1382574300.0
    num_examples: 600
  - name: test
    num_bytes: 2763509714.0
    num_examples: 1200
  download_size: 26944466147
  dataset_size: 27174356034.0
- config_name: unseen
  features:
  - name: id
    dtype: string
  - name: device_class
    dtype: string
  - name: array_id
    dtype: string
  - name: condition
    dtype: string
  - name: n_mics
    dtype: int32
  - name: n_samples
    dtype: int64
  - name: sir_db
    dtype: float32
  - name: snr_db
    dtype: float32
  - name: sensor_snr_db
    dtype: float32
  - name: rt60
    dtype: float32
  - name: drr_db
    dtype: float32
  - name: mic_positions
    sequence:
      sequence: float32
  - name: mix
    dtype:
      audio:
        sampling_rate: 48000
  - name: target
    dtype:
      audio:
        sampling_rate: 48000
  - name: enroll
    dtype:
      audio:
        sampling_rate: 48000
  splits:
  - name: test
    num_bytes: 3023450256.0
    num_examples: 1200
  download_size: 2983819608
  dataset_size: 3023450256.0
configs:
- config_name: seen
  data_files:
  - split: train
    path: seen/train-*
  - split: dev
    path: seen/dev-*
  - split: test
    path: seen/test-*
- config_name: unseen
  data_files:
  - split: test
    path: unseen/test-*
license: cc-by-nc-4.0
pretty_name: 'WEAVE: Cross-Device Array-Agnostic Wearable Speech Enhancement'
tags:
- speech-enhancement
- microphone-array
- beamforming
- wearable
- array-agnostic
- zero-shot
task_categories:
- audio-to-audio
language:
- en
---

# WEAVE — Cross-Device, Array-Agnostic Wearable Speech Enhancement Benchmark

WEAVE is a benchmark for **target-speaker speech enhancement on wearable microphone arrays** that
measures the hardest generalization question in the field: does a model trained on one set of
device geometries still work **zero-shot on unseen array geometries** it never saw in training?
Unlike prior wearable benchmarks that fix a single microphone layout (e.g. a 4-mic glasses array),
WEAVE spans **glasses, earbuds, and neckbands with 2–8 microphones**.

Generator code, docs, and evaluation: **https://github.com/pujariaditya/WEAVE**

## ⚠️ Licensing — please read first

WEAVE mixtures are **rendered from three source corpora that are non-commercial / custom-licensed**:
**EARS** (clean speech), **WHAM!** (ambient noise), and **TAU-SRIR DB** (real room impulse responses,
used only to derive reverberation *statistics*). The audio here is **derivative** of those sources and
therefore **inherits their non-commercial terms** — released under **CC BY-NC 4.0** for
**non-commercial research use only**. By using it you agree to comply with the upstream licenses:

| Corpus | Role in WEAVE | License | Source |
|---|---|---|---|
| EARS | clean target + interferer speech | non-commercial research | https://sp-uhh.github.io/ears_dataset/ |
| WHAM! (high_res) | ambient diffuse noise | CC BY-NC 4.0 | http://wham.whisper.ai/ |
| TAU-SRIR DB | reverberation statistics (RT60, DRR) | non-commercial research | https://zenodo.org/record/6408611 |

For a license-clean path, the **generator** (Apache-2.0) at the GitHub repo regenerates this benchmark
byte-identically from corpora you obtain yourself.

## The benchmark

- **13,000 utterances**, 48 kHz. Each is a simulated scene: one **frontal (0°) target speaker** +
  up to 5 interferers + diffuse noise + sensor noise, recorded by a device's mic array.
- **Zero-shot cross-device split** — the only variable between seen and unseen is device geometry:

| Config / split | Rows | Device classes | Mic counts |
|---|---|---|---|
| `seen` / train | 10,000 | glasses, earbuds | 2, 4, 6 |
| `seen` / dev | 600 | glasses, earbuds | 2, 4, 6 |
| `seen` / test | 1,200 | glasses, earbuds | 2, 4, 6 |
| `unseen` / test | 1,200 | glasses, earbuds, **neckband** | 3, 4, 6, 8 |

Train on **head-worn** devices (glasses/earbuds); the `unseen` split holds out a **torso-worn
neckband** (a different acoustic body) and an **unseen mic count (3)**.

## How it was built

Array-agnosticism lives in the **renderer**, not the corpora, so the same acoustic scene can be
materialized for any mic layout. Per utterance: `mix = reverberant_target + Σ interferers + diffuse
noise + sensor noise`. The direct path is a **parametric DRTF** (rigid-sphere head for glasses/earbuds,
two-sphere "snowman" torso+head for neckbands, + device coloration + propagation delay). Late reverb
is an **isotropic diffuse field synthesized from real TAU-SRIR statistics** (per-octave RT60, DRR),
with inter-mic coherence `sinc(2·f·d/c)`. **TAU audio never enters a mixture — only its statistics.**

## Audio format

`mix`, `target`, and `enroll` are stored as the HuggingFace **`Audio`** feature (WAV, 48 kHz), so you
can **listen to every clip in the dataset viewer**. `mix` is a **multichannel** WAV (one channel per
microphone); `target` (the clean dry reference at mic 0) and `enroll` are mono. Each signal is
**peak-normalized to 0.95** so nothing clips — this does not affect the benchmark because SI-SDR is
scale-invariant.

## Loading

```python
from datasets import load_dataset, Audio
import soundfile as sf, io, numpy as np

ds = load_dataset("RootAccess4Life/WEAVE", "seen", split="test")   # or config "unseen"
row = ds[0]
target = np.asarray(row["target"]["array"])       # [T] clean dry target at mic 0 (label)
enroll = np.asarray(row["enroll"]["array"])        # [Te] enrollment of the target speaker

# full multichannel mix (all microphones): read the raw WAV bytes
raw = ds.cast_column("mix", Audio(decode=False))
mix, sr = sf.read(io.BytesIO(raw[0]["mix"]["bytes"]))   # [T, n_mics]
mic_positions = np.asarray(row["mic_positions"], np.float32)  # [n_mics, 3], device-frame, centroid-relative
```

## Columns

| Column | Type | Notes |
|---|---|---|
| `id` | string | utterance id |
| `device_class` | string | glasses / earbuds / neckband |
| `array_id` | string | topology id (e.g. `0`, `eb2`, `nb8`) |
| `condition` | string | `<device_class>:<array_id>` |
| `n_mics`, `n_samples` | int | array shape |
| `mic_positions` | list&lt;list&lt;float&gt;&gt; | `[n_mics, 3]`, device-frame, centroid-relative (metres) |
| `mix` | **Audio** | multichannel WAV — the array recording (input) |
| `target` | **Audio** | mono WAV — dry direct-path target at mic 0 (the enhancement label / SI-SDR reference) |
| `enroll` | **Audio** | mono WAV — a different utterance of the target speaker |
| `sir_db`, `snr_db`, `sensor_snr_db`, `rt60`, `drr_db` | float | per-scene acoustic metadata |

## Task & evaluation

Recover `target` from `mix` (single-channel estimate of the frontal target). The headline metric is
the **cross-device generalization gap** — the drop from the `seen`-test score to the zero-shot
`unseen`-test score (small = geometry-robust) — alongside SI-SDR / STOI / PESQ improvement over the
noisy mic-0 input. An evaluation module (`weave-eval`) is provided in the GitHub repo.

## Limitations

Pure simulation (no real wearable-array recordings); **parametric acoustics, not measured HRTFs**; the
neckband "snowman" omits inter-sphere multiple scattering (documented v1 approximation); reverb is a
diffuse field synthesized from real room *statistics*, not full measured-RIR convolution; the dry
reference is a modeled target. Real-measured-device validation is future work.

## Citation

See `CITATION.cff` in the [GitHub repository](https://github.com/pujariaditya/WEAVE). Please also cite
the source corpora (EARS, WHAM!, TAU-SRIR DB).