DeepFilterNet vs Double Open Compliance

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.

Double Open Compliance automates software security and license compliance with deterministic filters and framework-anchored AI agents. It addresses legal due diligence for the EU Cyber Resilience Act, NIS2, and DORA. ORT Analyzer captures transitive dependencies according to how software is built, while Double Open Server provides global file-level caching that the site says enables 10x faster scanning. The platform produces machine-readable SBOMs in CycloneDX and SPDX formats and manages GitHub, GitLab, and air-gapped projects. AI agents surface ambiguous exceptions for human verification or version-controlled pull requests. Vulnerability triage ranks CVEs by code reachability and package roles and proposes evidence-backed resolutions. Deployment can be SaaS or customer-hosted, with data in EU-controlled datacenters or on customer infrastructure. The ORT stack includes a web UI, dashboards, REST API, and identity management.