Flacgain //top\\ -

We invite implementation in ffmpeg, sox, and open-source players. A reference Python library and a set of 50 test samples (classical, jazz, electronic, field recordings) are available at https://github.com/example/flacgain .

The MP3gain and ReplayGain standards successfully addressed the problem of perceived loudness normalization for lossy codecs (MP3, AAC, Ogg Vorbis) and lossless playback. However, these systems operate on a single global gain value per track or album, linearly scaling the entire waveform. This paper introduces , a novel extension to the FLAC (Free Lossless Audio Codec) ecosystem that goes beyond global loudness normalization. FLACgain analyzes a lossless stream to generate a perceptual dynamic range profile and encodes it as a reversible metadata sidechain. This allows a decoder or player to dynamically adjust gain on a short-term basis (e.g., per 50ms window) to achieve a consistent perceptual loudness envelope without crushing transient peaks or raising noise floors unnaturally. The result is an archive that retains perfect bit-identical reconstruction while offering an enhanced listening experience—especially for classical music, jazz, and film scores with extreme dynamics. flacgain

FLACgain is built on three pillars:

The original signal can be restored perfectly by applying the inverse gain sequence ( -g[i] ), demonstrating lossless reversibility. We invite implementation in ffmpeg, sox, and open-source

FLACgain extends the lossless audio archive from a static snapshot to an adaptive perceptual object . By moving dynamic range adjustment from destructive real-time processing to reversible, analyzed metadata, we give listeners control without compromising fidelity. A classical music lover can hear a ppp passage in a taxi; a producer can still verify the original mix in a studio. The file is the same. The experience is personal. However, these systems operate on a single global