| --- |
| license: other |
| language: |
| - en |
| tags: |
| - audio |
| - noise |
| - speech-enhancement |
| - denoising |
| - demand |
| - sparco |
| task_categories: |
| - audio-classification |
| - audio-to-audio |
| pretty_name: benchmark_DEMAND_noise |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # benchmark_DEMAND_noise |
|
|
| This dataset is a segmented subset derived from **DEMAND: Diverse Environments Multichannel Acoustic Noise Database**. |
|
|
| It is prepared for the SPARCO noise ablation benchmark. The intended use is to provide fixed 4-second environmental noise segments for: |
|
|
| - AUROC-based SAE noise-related feature selection |
| - binary noise-presence scorer training |
| - scorer threshold calibration |
| - final held-out benchmark evaluation |
|
|
| ## Source |
|
|
| Original source: |
|
|
| - DEMAND: Diverse Environments Multichannel Acoustic Noise Database |
| - Authors: Joachim Thiemann, Nobutaka Ito, Emmanuel Vincent |
| - DOI: 10.5281/zenodo.1227121 |
| - Source page: https://zenodo.org/records/1227121 |
|
|
| ## Important license notice |
|
|
| The original DEMAND Zenodo page and related listings should be consulted for the authoritative license terms. |
|
|
| At the time this dataset card was prepared, the DEMAND license information may appear as Creative Commons Attribution 4.0 in Zenodo metadata, while other descriptions/listings may refer to Creative Commons Attribution-ShareAlike 3.0. |
|
|
| Because of this ambiguity, this Hugging Face dataset card uses: |
|
|
| ```yaml |
| license: other |
| ``` |
|
|
| Users should verify the applicable DEMAND license before redistribution, commercial use, or publishing derived mixtures. If the ShareAlike interpretation applies, derived audio segments or mixtures may need to follow compatible ShareAlike terms. |
|
|
| ## Modifications from original DEMAND |
|
|
| The original DEMAND recordings were processed as follows: |
|
|
| - only 16 kHz folders were used |
| - only channel 1 was used |
| - audio was segmented into 4-second non-overlapping clips |
| - incomplete trailing segments were discarded |
| - segments were saved as 16-bit PCM WAV files |
| - metadata was generated for leakage-aware split tracking |
|
|
| No denoising, enhancement, normalization, or artificial mixing was applied in this dataset. |
|
|
| ## Split design |
|
|
| The dataset uses environment-level splitting to reduce leakage risk. The same DEMAND environment never appears in more than one split. |
|
|
| | Split | Purpose | DEMAND environments | |
| |---|---|---| |
| | feature_scorer_train | AUROC feature selection + noise presence scorer training | DKITCHEN, DLIVING, NFIELD, OMEETING, DWASHING, TMETRO, PRESTO, PSTATION, SPSQUARE, TCAR | |
| | scorer_val | scorer threshold calibration / checkpoint selection | OOFFICE, NPARK, TBUS | |
| | benchmark_test | final held-out evaluation only | NRIVER, OHALLWAY, STRAFFIC, PCAFETER | |
|
|
| ## Recommended protocol |
|
|
| Use `feature_scorer_train` for: |
|
|
| - selecting noise-related SAE features using AUROC |
| - training the binary noise-presence scorer |
|
|
| Use `scorer_val` for: |
|
|
| - choosing scorer thresholds |
| - selecting checkpoints |
| - selecting intervention strength or number of edited features |
|
|
| Use `benchmark_test` only once for final evaluation. |
|
|
| Do not use `benchmark_test` for: |
|
|
| - AUROC feature selection |
| - scorer training |
| - threshold tuning |
| - selecting K |
| - choosing intervention strength |
|
|
| ## Files |
|
|
| Each row in `metadata.csv` corresponds to one audio segment. |
|
|
| Important columns: |
|
|
| - `file_name`: relative path to the audio segment |
| - `split`: one of `feature_scorer_train`, `scorer_val`, `benchmark_test` |
| - `env`: DEMAND environment |
| - `channel`: channel used, currently `ch01` |
| - `source_group_id`: leakage group, equal to environment |
| - `temporal_group_id`: environment and time range identifier |
| - `start_sec`, `end_sec`: segment boundaries in the original channel file |
| - `source_file`: original local source file name |
| - `source_url`: DEMAND source URL |
| - `license_note`: license caution note |
|
|
| ## Example loading |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("SPARCO-project/benchmark_DEMAND_noise") |
| print(ds) |
| ``` |
|
|
| Or locally: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("audiofolder", data_dir="./benchmark_DEMAND_noise") |
| print(ds) |
| ``` |
|
|
| ## Citation |
|
|
| If you use this dataset, cite the original DEMAND database: |
|
|
| Thiemann, J., Ito, N., & Vincent, E. (2013). DEMAND: a collection of multi-channel recordings of acoustic noise in diverse environments. Zenodo. https://doi.org/10.5281/zenodo.1227121 |
|
|
