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Algorithmix Sound Laundry vs @audio/denoise

Algorithmix Sound Laundry

algorithmix.com

@audio/denoise

audiojs.dev

Side by side

FigureAlgorithmix Sound Laundry@audio/denoise
Price from—Free
Free plan—Yes
Free trial——
Open source—Yes
Web—Yes
WindowsYes—
Plans published02
Spec score · Audio Restoration Software7.6 (#13)8.4 (#1)
PurposeSound Laundry is a set of audio modules for cleaning and mastering scratchy and noisy recordings, especially old records and cassettes.The noise remover cleans recordings and lets users listen to and save the result.

Plans

Algorithmix Sound Laundry

No plans published.

Algorithmix Sound Laundry pricing →

@audio/denoise

  • FreeFree
  • Free browser noise removerFree

@audio/denoise pricing →

In their own words

Algorithmix Sound Laundry

Algorithmix Sound Laundry is a Windows software set for cleaning and mastering noisy or scratchy recordings, especially old records, cassettes, and other damaged audio. The modules can be bought separately, with additional plug-ins added later. The classic suite contains DeScratcher, DeNoiser, Analyzer, High/LowCut Filter, DC-Removal, and SignalControl. The advanced suite adds PEQclassic, DeRumble, and AlgoSpec. DeScratcher includes profiles for vinyl, shellac records, and digital clicks. DeNoiser can take a noise print from a marked AlgoPlayer block and display it graphically. AlgoPlayer provides waveform viewing, block processing, wave-file import and export, cutting, and cursor-based splitting. The PlugIn Station has ten slots, preset management, meters, and a wave recorder with timer and threshold controls, supporting sampling rates up to 96 kHz. It also offers dither and extended input/output options for CD-format output. Listed minimums include a Windows 95/98/ME/2000/NT 4.0 PC, 16-bit 44.1 kHz stereo sound card, and 10 MB disk space.

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@audio/denoise

@audio/denoise is a free web tool for cleaning recordings and listening to or saving the result. It handles targets including steady background noise, mains hum, clicks, pops, vinyl crackle, clipping, wind rumble, plosives, sibilance, room reverb, and noise between phrases. The page describes thirteen open-source restoration methods and a classifier. Automatic mode measures properties of a recording, selects a specialized method, and displays its plan; users can instead choose methods such as dehum, declick, declip, and gate. Audio is processed in the browser, and the page says it is not uploaded or sent off the device. The approach uses classical signal processing rather than AI noise removal. For music, the page identifies hum, clicks, crackle, and clipping as treatable, while advising light use of broadband denoising and comparison with the original. Machine-learning models may perform better on heavy speech-over-noise. The associated npm package processes mono Float32Array audio. Automatic mode does not choose dereverb, which must be selected explicitly.

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