APCodec+：一种采用分阶段训练范式的基于频谱编码的高保真高压缩率神经音频编解码器
APCodec+: A Spectrum-Coding-Based High-Fidelity and High-Compression-Rate Neural Audio Codec with Staged Training Paradigm
AI总结：
该研究提出改进型神经音频编解码器APCodec+，以音频幅度和相位谱为编码对象，采用两阶段联合-独立训练范式降低解码器学习难度，在低比特率下实现了与更高比特率基线编解码器相当的性能。
摘要：
本文提出了一种新型神经音频编解码器APCodec+，它是APCodec的改进版本。APCodec+以音频幅度谱和相位谱为编码对象，并采用对抗训练策略。我们创新性地为APCodec+提出了一种两阶段的联合-独立训练范式。在联合训练阶段，编码器、量化器、解码器和判别器通过完整的频谱损失、量化损失和对抗损失进行联合训练。在独立训练阶段，编码器和量化器固定其参数，为解码器和判别器提供高质量训练数据。解码器和判别器在无量化损失的情况下从头开始独立训练。引入独立训练的目的是降低解码器的学习难度，从而进一步提升解码音频的保真度。实验结果证实，得益于所提出的分阶段训练范式，我们提出的APCodec+在低比特率下可达到与更高比特率下的基线编解码器相当的性能。
英文摘要：
This paper proposes a novel neural audio codec, named APCodec+, which is an improved version of APCodec. The APCodec+ takes the audio amplitude and phase spectra as the coding object, and employs an adversarial training strategy. Innovatively, we propose a two-stage joint-individual training paradigm for APCodec+. In the joint training stage, the encoder, quantizer, decoder and discriminator are jointly trained with complete spectral loss, quantization loss, and adversarial loss. In the individual training stage, the encoder and quantizer fix their parameters and provide high-quality training data for the decoder and discriminator. The decoder and discriminator are individually trained from scratch without the quantization loss. The purpose of introducing individual training is to reduce the learning difficulty of the decoder, thereby further improving the fidelity of the decoded audio. Experimental results confirm that our proposed APCodec+ at low bitrates achieves comparable performance with baseline codecs at higher bitrates, thanks to the proposed staged training paradigm.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
