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Language models achieve lossless audio compression with new tokenization

Researchers have developed a new byte-level tokenization schema called Trilobyte for language models to achieve lossless compression of full-fidelity audio. This method addresses the vocabulary scaling issues that arise with higher bit depths in audio, enabling practical compression for 16-bit and 24-bit audio. While the approach shows state-of-the-art compression for 8-bit and 16-bit audio, outperforming existing codecs like FLAC, the gains become less significant as bit depth increases beyond 8-bit. AI

IMPACT This research demonstrates a novel application of language models for audio compression, potentially improving efficiency for high-fidelity audio storage and transmission.

RANK_REASON The cluster contains an academic paper detailing a new method for audio compression using language models. [lever_c_demoted from research: ic=1 ai=1.0]

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  1. arXiv cs.AI TIER_1 English(EN) · Phillip Long, Zachary Novack, Chris Donahue ·

    Benchmarking Language Modeling for Lossless Compression of Full-Fidelity Audio

    arXiv:2603.08683v2 Announce Type: replace-cross Abstract: Autoregressive "language" models (LMs) trained on raw waveforms can be repurposed for lossless audio compression, but prior work is limited to 8-bit audio, leaving open whether such approaches work for practical settings (…