High-Fidelity Generative Audio Compression at 0.275kbps
Hao Ma, Ruihao Jing, Shansong Liu, Cheng Gong, Chi Zhang, Xiao-Lei Zhang, and Xuelong Li

TL;DR
This paper introduces Generative Audio Compression (GAC), a novel approach that leverages semantic understanding and generative models to achieve high-fidelity audio reconstruction at ultra-low bitrates, surpassing existing codecs.
Contribution
The paper presents GAC, a task-oriented audio compression paradigm that uses powerful generative priors to drastically reduce bitrate while maintaining perceptual and semantic quality.
Findings
Achieves 0.275kbps bitrate for high-fidelity 32kHz audio
Outperforms state-of-the-art neural codecs in perceptual quality
Maintains intelligibility at 0.175kbps, 3000x compression ratio
Abstract
High-fidelity general audio compression at ultra-low bitrates is crucial for applications ranging from low-bandwidth communication to generative audio-language modeling. Traditional audio compression methods and contemporary neural codecs are fundamentally designed for waveform reconstruction. As a result, when operating at ultra-low bitrates, these methods degrade rapidly and often fail to preserve essential information, leading to severe acoustic artifacts and pronounced semantic distortion. To overcome these limitations, we introduce Generative Audio Compression (GAC), a novel paradigm shift from signal fidelity to task-oriented effectiveness. Implemented within the AI Flow framework, GAC is theoretically grounded in the Law of Information Capacity. These foundations posit that abundant computational power can be leveraged at the receiver to offset extreme communication…
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Taxonomy
TopicsDigital Filter Design and Implementation · Advanced Data Compression Techniques · Speech and Audio Processing
