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