# microsoft/DNS-Challenge

This repo contains the scripts, models, and required files for the Deep Noise Suppression (DNS) Challenge.

Repository: https://github.com/microsoft/DNS-Challenge
Canonical: https://ross.abutalabs.com/products/dns-challenge
Language: Python
License: CC-BY-4.0
License Family: other
Last push: 2024-07-25T10:19:06+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2423, "days_push": 769, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1465, forks 456 (observed 2026-08-28T04:04:48.238348+00:00)

## What it is
Microsoft's repository for the Deep Noise Suppression (DNS) Challenge, containing datasets, scripts, and baseline models for training speech enhancement models. It supports noise suppression, de-reverberation, and interfering talker suppression for headset and speakerphone scenarios.

## Use cases
- train a deep learning model to suppress background noise in speech
- build a speech enhancement model for headset calls
- denoise meeting recordings with interfering talkers
- evaluate noise suppression models with ITU-T P.835 metrics
- generate synthetic noisy speech training data
- compute speaker embeddings for personalized speech enhancement

## When to choose
- you are participating in the DNS Challenge or building speech enhancement models
- you need large-scale noisy/clean speech datasets for training denoising models
- you want standardized P.835-based evaluation of noise suppression quality

## When to avoid
- you need a ready-to-use noise suppression product rather than training resources
- you work with non-speech audio like music denoising
- you need real-time noise suppression in a production app without training your own model

## Facets
- artifact type: dataset
- maturity: active
- function: audio-processing, machine-learning, speech-recognition, benchmarking, data-generation
- domain: speech-processing, machine-learning, artificial-intelligence
- platform: python, cross-platform
- tags: noise-suppression, speech-enhancement, dns-challenge, dereverberation, audio-datasets, icassp, deep-learning, audio

## Member repositories
- microsoft/DNS-Challenge (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:48.238348+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:35:06.396486+00:00, confidence not recorded.
  - readme: https://github.com/microsoft/DNS-Challenge (fetched 2026-08-28T04:04:48.238348+00:00, sha 28dabba88359)
- Data as of 2026-08-30T08:39:29.467469+00:00.