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 show that this general methodology, easily applicable to any NAC, enables a substantial gain in bitrate-distortion performance, as well as a massive reduction in complexity.

翻译：神经音频编解码器（NACs）通常将语音/音频信号的短时能量（增益）和归一化结构（形状）共同编码在同一潜在空间中。因此，它们对输入信号电平的全局变化鲁棒性较差，因为这种变化会强烈影响编码器输出的嵌入向量及其量化。这种方法本质上是低效的，会导致码本冗余和次优的码率-失真性能。为了解决这些限制，我们提出将经典语音/音频编码中广泛使用的形状-增益分解引入NAC框架。所提出的均衡器方法的基本原理是：在NAC编码器之前，将输入信号在短时基础上分解为增益和归一化形状向量。形状向量由NAC处理，而增益则通过标量量化并单独传输。输出（解码）信号由NAC的归一化输出和量化后的增益重建而成。我们在语音信号上进行的实验表明，这种通用方法（可轻松应用于任何NAC）能够显著提升码率-失真性能，并大幅降低复杂度。