论文标题
一种低复杂性的感知动机方法
A Perceptually-Motivated Approach for Low-Complexity, Real-Time Enhancement of Fullband Speech
论文作者
论文摘要
在过去的几年中，基于深度学习的语音增强方法已大大超过了基于光谱减法和光谱估计的传统方法。这些新技术中的许多直接在短时的傅立叶变换（STFT）域中运行，从而产生了较高的计算复杂性。在这项工作中，我们提出了一种感知者，这是一种有效的方法，它通过关注光谱信封和言语的周期性来依赖于人类对语音的看法。我们证明了高质量的高质量，实时增强（48 kHz）语音，不到CPU核心的5％。
Over the past few years, speech enhancement methods based on deep learning have greatly surpassed traditional methods based on spectral subtraction and spectral estimation. Many of these new techniques operate directly in the the short-time Fourier transform (STFT) domain, resulting in a high computational complexity. In this work, we propose PercepNet, an efficient approach that relies on human perception of speech by focusing on the spectral envelope and on the periodicity of the speech. We demonstrate high-quality, real-time enhancement of fullband (48 kHz) speech with less than 5% of a CPU core.
