--- 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 | `:` | | `n_mics`, `n_samples` | int | array shape | | `mic_positions` | list<list<float>> | `[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).