AhanaBeats applies neural arithmetic coding trained specifically on music — capturing harmonic structure, rhythmic patterns, and tonal relationships that general-purpose codecs treat as noise.
A drum pattern repeats. A chord progression follows harmonic rules. A melody has phrase structure. Classical codecs see a waveform. AhanaBeats sees the music.
The AhanaBeats neural model is trained exclusively on music — spanning genres, tempos, and instrumentation. It develops an internal model of what musical patterns look like, enabling sharper predictions than a general audio codec.
Repeating rhythmic patterns are highly predictable. AhanaBeats assigns them very low coding cost — the model learns that the next beat is likely similar to the previous one.
Chords and harmonic progressions follow constraints that allow the model to predict spectral content across time. A note in the key of C major prediction space is drastically smaller than the full chromatic space.
AhanaBeats outputs streaming-compatible .aarm containers. Start playback before the file finishes downloading. Compatible with platforms that need progressive delivery.
Compress stems individually into a single multi-track .aarm container. Cross-stem correlations (kick and bass, vocals and reverb) further reduce total file size.
FLAC is the gold standard for lossless music. AhanaBeats targets consistent improvement over FLAC on music content — the same perfect quality, fewer bytes.
AhanaBeats is built for producers, studios, and streaming platforms. Join early access to help shape the product.
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