标题： 基于波形域神经网络的Ambisonics超分辨率 显示英文标题

标题： Ambisonics Super-Resolution Using A Waveform-Domain Neural Network

摘要： Ambisonics是一种描述声场的空间音频格式。 一阶Ambisonics（FOA）是一种流行的格式，仅包含四个通道。 这种有限的通道数量以空间准确性为代价。 理想情况下，人们能够利用FOA格式的效率，而不受其限制。 我们设计了一种数据驱动的空间音频解决方案，保留了FOA格式的效率，但实现了超越传统渲染器的质量。 利用一个全卷积时域音频神经网络（Conv-TasNet），我们创建了一个解决方案，该方案接受FOA输入并提供更高阶的Ambisonics（HOA）输出。 与典型的基于物理和心理声学的渲染器相比，这种数据驱动的方法是新颖的。 定量评估显示，预测的和实际的第三阶HOA之间的平均位置均方误差差异为0.6dB。 中位数定性评分显示，相对于传统的渲染方法，感知质量提高了80%。 显示英文摘要

摘要： Ambisonics is a spatial audio format describing a sound field. First-order Ambisonics (FOA) is a popular format comprising only four channels. This limited channel count comes at the expense of spatial accuracy. Ideally one would be able to take the efficiency of a FOA format without its limitations. We have devised a data-driven spatial audio solution that retains the efficiency of the FOA format but achieves quality that surpasses conventional renderers. Utilizing a fully convolutional time-domain audio neural network (Conv-TasNet), we created a solution that takes a FOA input and provides a higher order Ambisonics (HOA) output. This data driven approach is novel when compared to typical physics and psychoacoustic based renderers. Quantitative evaluations showed a 0.6dB average positional mean squared error difference between predicted and actual 3rd order HOA. The median qualitative rating showed an 80% improvement in perceived quality over the traditional rendering approach.