DeepFilterNet vs Mend SCA
DeepFilterNet
github.com
Mend SCA
mend.ioSide by side
| Figure | DeepFilterNet | Mend SCA |
|---|---|---|
| Price from | Free | $20.83/mo |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | Yes | — |
| Web | — | Yes |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 2 | 3 |
| Spec score · Software Composition Analysis Software | 8.9 (#1) | 8.6 (#6) |
| Purpose | DeepFilterNet is a low-complexity speech enhancement framework for full-band 48 kHz audio using deep filtering. | Mend SCA identifies, prioritizes and remediates security and license risks in applications’ open source components. |
| What it does | DeepFilterNet is a low-complexity speech-enhancement framework that uses deep filtering for full-band audio. | Mend SCA analyzes open-source dependencies for vulnerabilities, license risks and malicious packages, then prioritizes and remediates findings. |
Plans
Mend SCA
- Mend Renovate Enterprise$250 a year
- Mend AI$300 a year
- Mend AppSec$1,000 a year
In their own words
DeepFilterNet
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.