Accentize DeRoom vs DeepFilterNet

Accentize DeRoom is a machine-learning audio plugin for reducing reverb and room resonances in recordings. It is intended to improve speech quality and intelligibility when reflections and echoes affect a recording. Its neural network distinguishes direct sound from reflections, detects reverb tails and echoes, and suppresses them. DeRoom estimates room timing constants automatically; the user chooses the reduction amount. The standard license includes reverb removal and room resonance suppression. DeRoom Pro adds spectral focus, attack and release controls, reverb-only monitoring, separate left and right processing, and spectrogram and gain visualization. Both versions support mono and stereo audio in VST3, AU, and AAX formats, with listed hosts including Pro Tools, Cubase, Nuendo, Reaper, Ableton Live, Logic Pro, Adobe Premiere, Final Cut, and DaVinci Resolve. DeRoom runs locally, and its serial-key license works offline. Pro uses iLok and requires a free iLok account. The software runs on macOS and Windows. DeRoom costs €49, paid once; DeRoom Pro costs €249, paid once. A seven-day free trial is offered.

DeepFilterNet is a free, open-source framework for speech enhancement on full-band 48 kHz audio, using deep filtering. Its command-line tool suppresses noise in WAV files, with 48 kHz as its only supported sampling rate. A LADSPA plugin provides real-time noise suppression and can be used with PipeWire filter chains to create a virtual microphone or audio output, including for VoIP apps such as Zoom or Discord. The plugin's minimum latency is 20 ms from STFT processing, plus any delay added by the LADSPA host. The Rust command-line tool defaults to DeepFilterNet2; the Python interface documents DeepFilterNet and DeepFilterNet2. An optional post-filter may slightly over-attenuate very noisy sections. The framework supports Linux, macOS and Windows, though training is tested only under Linux. The Python package installs as deepfilternet, and an optional train extra adds data-loading functionality for Linux training. Code is available under either the MIT License or Apache License 2.0.