Preset normalization and comparison for audio processors
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Updated
Jul 4, 2026 - Python
Preset normalization and comparison for audio processors
A refined NetEase CloudMusic desktop client — high-fidelity streaming, word-by-word lyrics, spectrum visualization, loudness normalization, and cloud-local sync.
Desktop GUI application for converting lossless and high-resolution audio to ALAC (.m4a), with metadata and artwork editor, lookup, and loudness normalization. (Supported Languages: 日本語, English, 简体中文, 繁體中文, 한국어, 朝鮮語(國漢文))
Generic Broadcast Automation System
C++ Procedural Audio Mixing Engine (DSP) - Deterministic multi-layer audio mixing system implementing frequency-aware gain compensation, role-based layer weighting, LUFS loudness normalization, adaptive gain staging, transparent limiter, and real-time/offline rendering for procedural/generative audio systems.
Extension navigateur open source pour streamers et powerusers qui aide à réduire les écarts de volume et les pics audio localement, sans tracker et sans collecte de données.
Automate your LoFi track processing: smart silence trimming, crossfaded mixing, and acoustic repeat detection powered by FFmpeg.
NORM! 🍻 Loudness normalization for unruly MP3 archives.
Windows tool for honest audio analysis, gentle FFmpeg processing, batch export and result validation.
Windows-first PySide6 MP3Gain GUI/CLI port with a vendored legacy C DLL backend for fast parallel ReplayGain analysis and gain application.
Ultra-lightweight CLI audio player for Windows supporting file and stdin input, EBU R128 loudness normalization, and BLE guard tones
Command line tool to combine two audio files into one
Audio focused YouTube downloader and processing library for Go
Fix quiet videos from Slack and macOS screen recordings on Linux — CLI + Nautilus right-click script
Automatic mixing and mastering for finished stems. A deterministic, open-source Python engine that loudness-balances stems into a stereo mix and masters it to an exact LUFS target with a true-peak limiter (ITU-R BS.1770-4). Same stems in, same master out — no AI, no subscription.
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