Reconstructing the room transfer functions needed to calculate the complex
sound field in a room has several important real-world applications. However,
an unpractical number of microphones is often required. Recently, in addition
to classical signal processing methods, deep learning techniques have been
applied to reconstruct the room transfer function starting from a very limited
set of room transfer functions measured at scattered points in the room. In
this study, we employ complex-valued neural networks to estimate room transfer
functions in the frequency range of the first room resonances, using a few
irregularly distributed microphones. To the best of our knowledge, this is the
first time complex-valued neural networks are used to estimate room transfer
functions. To analyze the benefits of applying complex-valued optimization to
the considered task, we compare the proposed technique with a state-of-the-art
real-valued neural network method and a state-of-the-art kernel-based signal
processing approach for sound field reconstruction, showing that the proposed
technique exhibits relevant advantages in terms of phase accuracy and overall
quality of the reconstructed sound field.

利用复数神经网络在频率范围内估计房间传递函数，通过少量分布不规则的麦克风的测量，与传统方法和基于核的信号处理方法进行对比，展示了提出的技术在相位准确性和重建声场整体质量方面的重要优势。

利用复值神经网络和不规则分布麦克风重建房间传递函数

Room transfer function reconstruction using complex-valued neural  networks and irregularly distributed microphones

This paper investigates the effects of data size and frequency range on
distributional semantic models. We compare the performance of a number of
representative models for several test settings over data of varying sizes, and
over test items of various frequency. Our results show that neural
network-based models underperform when the data is small, and that the most
reliable model over data of varying sizes and frequency ranges is the inverted
factorized model.

本研究旨在探究数据规模和频率范围对分布语义模型的影响。比较了几种代表性模型在不同规模的数据和各种不同频率的测试项下的性能表现。结果表明，当数据规模较小时，基于神经网络的模型表现不佳，而在各种规模和频率范围的数据下，可靠性最高的模型是倒置分解模型。