---
license: apache-2.0
task_categories:
- audio-classification
tags:
- room-impulse-response
- audio-augmentation
- rir
- noise
pretty_name: RIRS NOISES
size_categories:
- 1K<n<10K
configs:
- config_name: default
  data_files:
  - split: train
    path: "**/*.wav"
---

# RIRS NOISES

## Quick Start

```python
from datasets import load_dataset

# Stream to avoid downloading the entire dataset
ds = load_dataset("schism-audio/rirs-noises", streaming=True)

# Or download locally
ds = load_dataset("schism-audio/rirs-noises")
```

## Dataset Description

**RIRS NOISES** is the real/isotropic RIR and point-source noise subset from [OpenSLR SLR28](https://openslr.org/28/). This Hugging Face repo contains 1,260 WAV files: 417 files under `real_rirs_isotropic_noises/` and 843 files under `pointsource_noises/`. All audio is 16 kHz WAV.

This subset is useful for audio data augmentation: convolving clean audio with real RIRs to simulate reverberant environments and adding noise at controlled SNRs. For the full 61,260-file OpenSLR SLR28 mirror, including simulated RIRs, see [`schism-audio/openslr-rirs`](https://huggingface.co/datasets/schism-audio/openslr-rirs).

## Dataset Structure

### Data Fields

| Field | Type | Description |
|-------|------|-------------|
| `audio` | `Audio` | WAV file at 16 kHz, 16-bit |
| `label` | `ClassLabel` | Top-level source directory: `pointsource_noises` or `real_rirs_isotropic_noises` |

### Data Splits

This dataset has no predefined splits. All files are in the default `train` split.

| Split | Files |
|-------|------:|
| `train` | 1,260 |

File counts by directory:

| Directory | Files |
|-----------|------:|
| `pointsource_noises/` | 843 |
| `real_rirs_isotropic_noises/` | 417 |

## Usage Examples

### Load RIRs for augmentation

```python
from datasets import load_dataset

ds = load_dataset("schism-audio/rirs-noises")

# Filter for real RIRs only
rirs = ds["train"].filter(
    lambda x: ds["train"].features["label"].int2str(x["label"]) == "real_rirs_isotropic_noises"
)
print(f"Real RIRs: {len(rirs)}")
```

### Convolve audio with an RIR

```python
import numpy as np
from scipy.signal import fftconvolve
from datasets import load_dataset

ds = load_dataset("schism-audio/rirs-noises")
rir = ds["train"][0]["audio"]["array"]

# Normalize the RIR
rir = rir / np.max(np.abs(rir))

# Convolve with your dry audio signal
# reverberant = fftconvolve(dry_audio, rir, mode="full")
```

## Dataset Creation

### Source Data

The dataset was compiled from multiple publicly available acoustic databases:

- **Real/isotropic RIR and noise files:** 417 WAV files under `real_rirs_isotropic_noises/`.
- **Point-source noises:** 843 WAV files under `pointsource_noises/`.

### Annotations

No manual annotations are included. File organization and naming encode the source database and recording type.

## Known Limitations

- **Speech-centric design:** The RIRs and noise profiles were originally curated for speech processing. Drum and music applications may require additional filtering or selection.
- **16 kHz only:** The fixed 16 kHz sample rate may be limiting for music applications that require higher fidelity.
- **Subset only:** This repo does not include the 60,000 simulated RIR files from OpenSLR SLR28. Use [`schism-audio/openslr-rirs`](https://huggingface.co/datasets/schism-audio/openslr-rirs) for the complete mirror.

## Related Datasets

This dataset is part of the [Drum Audio Datasets](https://huggingface.co/collections/schism-audio/drum-audio-datasets) collection by [schism-audio](https://huggingface.co/schism-audio). Related datasets:

- [schism-audio/dechorate](https://huggingface.co/datasets/schism-audio/dechorate) — Calibrated multichannel RIRs with echo annotations and 3D positions

## Citation

```bibtex
@misc{rirs_noises,
  title     = {A database of room impulse responses and noise recordings},
  author    = {Ko, Tom and Peddinti, Vijayaditya and Povey, Daniel and Seltzer, Michael L. and Khudanpur, Sanjeev},
  year      = {2017},
  url       = {https://openslr.org/28/}
}
```

## License

This dataset is released under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).

You are free to use, modify, and distribute this dataset for any purpose, including commercial use, subject to the terms of the Apache 2.0 license.