Audio denoising and dereverberation · XLANCE-MSR · TelkNet

Separation & stem splitGPU workloadMusic-Source-Separation-Training · Source UVR_resources · Weights

Audio denoising and dereverberation

Reduce noise, crowd sounds or reverb in recordings, compare with the original and download the processed audio.

25 credits/useFile inputMax 100 MBEstimated 60-1800 seconds

Input

Supports inputs in formats such as mp3 / wav / flac / ogg / m4a / aac / aif / aiff / mp4 / mov / mkv

Supported formats

mp3wavflacoggm4aaacaifaiffmp4movmkv

Output

WAV

Use case

Clean up recordings with noise, echo or live interference.

Choose a model family

Music denoise & dereverb · XLANCE-MSRAvailableCleaning a complete mix before mastering, separation, transcription, or video remux.Music denoising · MelBand RoFormerAvailableClean noise from music recordings; compare standard and aggressive versions for remaining noise and lost detail.Vocal De-Echo & Dereverb · MelBand RoFormerAvailableReturn a dry vocal and the removed echo-and-reverb residual from an isolated vocal recording.Crowd Removal · Crowd MelBand RoFormerAvailableSeparate crowd sound from a live recording and return both the crowd and the crowd-removed programme audio.

Choose a version or processing mode

Music Denoise · MelBand RoFormer · StandardStandard denoising weights for cleaning noise from music recordings.RecommendedAvailableMusic Denoise · MelBand RoFormer · AggressiveAggressive denoising weights for comparison with the standard result.Available

Choose a model for your material and purpose, and review the examples before submitting.

Cost, time, formats and parameters depend on the selected model.

Quick feedback

Include the tool name, steps and the problem in your feedback. Screenshots can help us locate it.

Models, parameters, and sources

MelBand RoFormer · Standard: Clean up recordings with noise, echo or live interference.

XLANCE-MSRMSR Challenge 2025 official resultsxlancelabCUPAudioGroupAC_DCHachimicp-jkuXLANCE-MSR Technical Report for MSR Challenge 2025A Summary of the First Music Source Restoration ChallengeMelBand RoFormer · StandardOfficial checkpoint filenamesDenoise MelBand RoFormer

XLANCE-MSR

XLANCE-MSR extends a six-stem BS-RoFormer system into eight-class source restoration with separation, denoising, and dereverberation/restoration. The MIT checkpoint weights are public.

XLANCE-MSR GitHub XLANCE-MSR MIT checkpoints MSR Challenge 2025 results XLANCE-MSR arXiv MSR Challenge summary

Targets eight source classes: vocals, guitar, keys, synth, bass, drums, percussion, and orchestral.
Official MSR Challenge 2025 results report objective rank #1 with MMSNR 4.4623, Zimt 0.0137, FAD 0.1988, and subjective overall MOS 3.4665. These are source-restoration metrics, not six-stem SDR.
Writes WAV stems only; the sound can be drier and closer to untreated material rather than preserving all record reverb, compression, EQ, mastering, and codec artifacts.
Score source: MSR Challenge 2025 ranked XLANCE-MSR first on objective and subjective source-restoration results; read MMSNR, Zimt, FAD, and MOS separation/restoration, not six-stem SDR.
Evaluation scope: this is eight-class source restoration / eight-stem restoration, scored on the MSR restoration task rather than merged into MVSep six-stem SDR rankings.

MelBand RoFormer · Standard

MSST inference source (pinned)Official denoise checkpoint snapshot

MelBand RoFormer · Aggressive

MelBand RoFormer · Aggressive: Clean up recordings with noise, echo or live interference.

MSST inference source (pinned)Official aggressive denoise checkpoint snapshot

Vocal De-Echo MelBand RoFormer

MSST inference source (pinned)Official de-echo checkpoint (pinned)

Crowd MelBand RoFormer

MSST inference source (pinned)Official crowd checkpoint snapshot

Paper and benchmark notes

XLANCE-MSR Technical Report for MSR Challenge 2025

arXiv - 2026

Technical report for the XLANCE-MSR system submitted to the first Music Source Restoration Challenge.

arXiv:2602.09042 Official challenge page

A Summary of the First Music Source Restoration Challenge

arXiv - 2026

Challenge summary paper describing the MSR task, datasets, objective metrics, subjective evaluation, and final ranking tables.

arXiv:2601.04343 MSR Challenge

Official / repositories

XLANCE-MSR GitHub XLANCE-MSR MIT checkpoints MSR Challenge 2025 results MSST inference source (pinned)Official denoise checkpoint snapshot Official aggressive denoise checkpoint snapshot Official de-echo checkpoint (pinned)Official crowd checkpoint snapshot

Papers / technical notes

XLANCE-MSR arXiv MSR Challenge summary

Parameter guide

Official MSST test-shift count. Higher values cost more time and may improve stability.
Official test-time augmentation. It increases processing time and is disabled by default.

Workflow

1Upload noisy music and choose standard or aggressive denoising.
2Compare the clean audio with the noise residual for lost musical details.

What to check

Prefer the highest-quality source available
Compare original and processed audio at matched loudness

Models in this field

2 output categories

Only records with verified public inference implementations and matching weights are shown; algorithms requiring no weights are also eligible. Closed services, unreleased weights and unmatched experiment versions are excluded. This is not an all-model leaderboard.

Published evaluation conditions

Each set of weights appears once per benchmark, using its best published result for the selected metric. An overall score comes from one evaluation. Expand to compare evaluations and settings.

Public weights do not establish a fully open-source license. Expand the data for declared weight licenses and access conditions. Undeclared licenses are marked separately.

Markers identify models or components used here. Scores and settings come from the cited tests, not a new evaluation of the complete site pipeline.

No comparable public results with verified code and matching weights are available.