README.md

metadata

license: cc-by-4.0
task_categories:
  - audio-classification
tags:
  - audio
  - noise
  - rir
  - augmentation
  - dns-challenge
  - microphone
  - impulse-response
pretty_name: DNS Noise + RIR (24k mono)
configs:
  - config_name: default
    data_files:
      - split: noise
        path: noise/*.parquet
      - split: rir
        path: rir/*.parquet
      - split: mic_ir
        path: mic_ir/*.parquet

DNS-Noise

DNS-Challenge noise_fullband and impulse_responses republished as 24 kHz mono FLAC for on-demand streaming augmentation, plus a microphone-characteristics split. Three splits:

• noise — environmental noise (long files segmented into chunks)
• rir — room impulse responses (one row per file)
• mic_ir — microphone impulse responses (one row per file)

from datasets import load_dataset
noise  = load_dataset("ChristianYang/DNS-Noise", split="noise",  streaming=True)
rir    = load_dataset("ChristianYang/DNS-Noise", split="rir",    streaming=True)
mic_ir = load_dataset("ChristianYang/DNS-Noise", split="mic_ir", streaming=True)

mic_ir split

Microphone impulse responses for simulating microphone frequency colouration, aggregated from three sources. source_file is prefixed with the source dataset:

prefix	source	content
madir/	Zenodo 4633508 — Multi-Angle, Multi-Distance Microphone Impulse Response Dataset (CC-BY-4.0)	studio mics, Raw_IRs only, one bit-depth variant per IR (24-bit preferred); folder names encode mic model, polar pattern, distance and angle
micirp/	MicIRP via audb v1.0.0 (CC BY-SA 4.0)	vintage/classic microphones
ctf_2020_tiny_irs/	Collected Transients "Tiny IRs" (2020)	consumer-device microphone captures (cellphone/tablet/laptop/camera mics, incl. mic-through-speaker chains); pure loudspeaker IRs are excluded

Processing: mixed down to mono, resampled to 24 kHz (soxr HQ), peak-normalised to 0.97, encoded as 16-bit FLAC. Absolute level is not meaningful — normalise IR energy before convolution.