IF 8.7 1区 工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Lars Villemoes;Mark Vinton;Per Ekstrand;Lie Lu;Grant Davidson;Cong Zhou
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引用次数: 0

摘要

我们描述并评估了由感知音频编码器和生成模型 MDCTNet 组成的混合神经音频编码系统。通过应用递归层(RNN),我们在感知加权自适应修正离散余弦变换(MDCT)域中捕捉时间和频率方向上的相关性。通过在 48 kHz 采样的各种全音域单声道音频信号上训练 MDCTNet,我们实现了与 24 kb/s 可变比特率 (VBR) 的最先进音频编码相媲美的性能。我们还量化了基于生成模型的解码在较低和较高比特率下的效果,并讨论了在音频编码任务中使用数据驱动信号重建的一些注意事项。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MDCTNet: A Hybrid Approach to Neural Audio Coding
We describe and evaluate a hybrid neural audio coding system consisting of a perceptual audio encoder and a generative model, MDCTNet. By applying recurrent layers (RNNs) we capture correlations in both time and frequency directions in a perceptually weighted adaptive modified discrete cosine transform (MDCT) domain. By training MDCTNet on a diverse set of full-range monophonic audio signals at 48 kHz sampling, we achieve performance competitive with state-of-the-art audio coding at 24 kb/s variable bitrate (VBR). We also quantify the effect of the generative model-based decoding at lower and higher bitrates and discuss some caveats of the use of data driven signal reconstruction for the audio coding task.
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来源期刊
IEEE Journal of Selected Topics in Signal Processing
IEEE Journal of Selected Topics in Signal Processing 工程技术-工程:电子与电气
CiteScore
19.00
自引率
1.30%
发文量
135
审稿时长
3 months
期刊介绍: The IEEE Journal of Selected Topics in Signal Processing (JSTSP) focuses on the Field of Interest of the IEEE Signal Processing Society, which encompasses the theory and application of various signal processing techniques. These techniques include filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals using digital or analog devices. The term "signal" covers a wide range of data types, including audio, video, speech, image, communication, geophysical, sonar, radar, medical, musical, and others. The journal format allows for in-depth exploration of signal processing topics, enabling the Society to cover both established and emerging areas. This includes interdisciplinary fields such as biomedical engineering and language processing, as well as areas not traditionally associated with engineering.
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