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[2602.15491] The Equalizer: Introducing Shape-Gain Decomposition in Neural Audio Codecs
Summary
The paper presents Shape-Gain Decomposition for Neural Audio Codecs, enhancing bitrate-distortion performance and reducing complexity by separating gain and shape processing.
Why It Matters
This research addresses inefficiencies in current neural audio codecs by introducing a decomposition method that improves performance and reduces redundancy. As audio processing becomes increasingly important in AI applications, these advancements could lead to more efficient audio encoding and better user experiences in various technologies.
Key Takeaways
- Introduces Shape-Gain Decomposition to improve neural audio codecs.
- Enhances bitrate-distortion performance significantly.
- Reduces complexity in audio signal processing.
- Separation of gain and shape allows for more efficient encoding.
- Methodology is applicable to any neural audio codec.
Computer Science > Sound arXiv:2602.15491 (cs) [Submitted on 17 Feb 2026] Title:The Equalizer: Introducing Shape-Gain Decomposition in Neural Audio Codecs Authors:Samir Sadok, Laurent Girin, Xavier Alameda-Pineda View a PDF of the paper titled The Equalizer: Introducing Shape-Gain Decomposition in Neural Audio Codecs, by Samir Sadok and 2 other authors View PDF HTML (experimental) Abstract:Neural audio codecs (NACs) typically encode the short-term energy (gain) and normalized structure (shape) of speech/audio signals jointly within the same latent space. As a result, they are poorly robust to a global variation of the input signal level in the sense that such variation has strong influence on the embedding vectors at the output of the encoder and their quantization. This methodology is inherently inefficient, leading to codebook redundancy and suboptimal bitrate-distortion performance. To address these limitations, we propose to introduce shape-gain decomposition, widely used in classical speech/audio coding, into the NAC framework. The principle of the proposed Equalizer methodology is to decompose the input signal -- before the NAC encoder -- into gain and normalized shape vector on a short-term basis. The shape vector is processed by the NAC, while the gain is quantized with scalar quantization and transmitted separately. The output (decoded) signal is reconstructed from the normalized output of the NAC and the quantized gain. Our experiments conducted on speech signals...
