Design of Medium to Low Bitrate Neural Audio Codec

Samarpreet Singh, Saurabh Singh Raghuvanshi, Vinal Patel
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Abstract

Neural audio codecs are the most recent development in the field of audio compression. Traditional audio codecs rely on fixed signal processing pipelines and require domain-specific expertise to produce high-quality audio at low to high bit rates. However, the performance of conventional audio codecs usually degrades at low bit rates. Neural audio codecs perform enhancement and compression with no added latency. This paper further enhances the quality of neural audio codecs by integrating a psychoacoustic model with the existing structure that contains a convolutional encoder, decoder, and a residual vector quantizer. It used a combination of reconstruction and adversarial loss to train the model to generate high-quality audio content. Audio quality measures like PEAQ and MUSHRA are conducted to illustrate that the proposed model performs better than the existing model of neural audio codec.
中低比特率神经音频编解码器的设计
神经音频编解码器是音频压缩领域的最新发展。传统的音频编解码器依赖于固定的信号处理管道,需要特定领域的专业知识才能以低到高比特率产生高质量的音频。然而,传统的音频编解码器的性能通常在低比特率下下降。神经音频编解码器执行增强和压缩,没有增加延迟。本文通过将心理声学模型与包含卷积编码器、解码器和残差矢量量化器的现有结构相结合,进一步提高了神经音频编解码器的质量。它结合了重建和对抗损失来训练模型以生成高质量的音频内容。通过PEAQ和MUSHRA等音质测量表明,该模型比现有的神经音频编解码器模型具有更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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