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Finesse De-esser v1.0

20 Jun 20:14
@tdon01 tdon01
v1.0.0
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Finesse De-esser

Finesse is a macOS de-esser which gives you full surgical control over sibilance reduction rather than a blunt broadband notch.
screenshot

Why Finesse exists

I make classical recordings primarily in churches, soprano and piano, choral works etc. I own many de-esser plugins but have not been happy with the results, and found I had to add automation to avoid false hits. This is a time consuming exercise, so I decided to build my own application to suit my preferred wokflow. I'm very happy with how it turned out and hope you will like it too.

How it works

  • Target Match — set one example of a typical ess, press set target ess and scan. The detector matches future events against it by spectral fingerprint
  • Detection Sliders — adjust the detection sliders and rescan until you're happy with the quantity and quality of detected ess events
  • Per-event review — step through each detected event (L&R arrows), audition and adjust edges if required, delete false positives, add missed events and adjust reduction amount globally and/or per event
  • Wideband or filtered reduction — choose filtered if you want to keep out-of-band audio, else wideband for best results
  • Session persistence — save your scan, edits, and settings alongside the audio file and pick up where you left off
  • Non-destructive — always writes a new file with a _deessed suffix; your original is never touched

Installation

  1. Download the latest .dmg from Releases
  2. Open the DMG and drag Finesse De-esser.app to Applications
  3. First launch: this build is not signed or notarised by Apple, so Gatekeeper will warn that the developer can't be verified. Right-click (Control-click) the app and choose Open, then confirm.
  4. If macOS reports the app as "damaged" instead, this is the same Gatekeeper restriction — clear it by running in Terminal: 
    xattr -dr com.apple.quarantine "/Applications/Finesse De-esser.app"

Full terms and disclaimers are in DISCLAIMER.txt — please read before use.

System requirements

Apple Silicon Mac (M1 or later), macOS 14 (Sonoma) or newer.

This build does not run on Intel Macs or under Rosetta — there's no x86_64 slice included. The macOS 14 floor comes from the bundled dependencies (NumPy/SciPy require Sonoma; PyQt6/soundfile alone would run on older macOS, but the strictest dependency sets the real floor). Built and tested on macOS Sequoia.

Basic workflow

  1. Open your audio file
  2. Shift-drag to select a clean, representative ess sound, then Set Target Ess
  3. Scan file to detect all sibilant events
  4. Adjust detection settings and rescan if needed
  5. Step through events, review, delete any false positives
  6. Export the processed file

Status

Version 1.0. The detection pipeline has been refined through extensive listening tests on real classical recordings and is considered stable. Feedback on real-world material is very welcome via Issues — particularly on source material that doesn't behave as expected.

Support

This is independently developed, free software with no support obligation, but feedback is always welcome.

If you find it useful: buymeacoffee.com/terrydon

License

Source code is not published. The binary is distributed for personal use under the terms in DISCLAIMER.txt. All rights reserved.

Built with Python, PyQt6, NumPy, SciPy, libsndfile (via soundfile), and PortAudio (via sounddevice).

© 2026 Terry Donoghue

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