Knowledge of loudspeaker responses are useful in a number of applications, where a sound system is located inside a room that alters the listening experience depending on position within the room. Acquisition of sound fields for sound sources located in reverberant rooms can be achieved through labor intensive measurements of impulse response functions covering the room, or alternatively by means of reconstruction methods which can potentially require significantly fewer measurements. This paper extends evaluations of sound field reconstruction at low frequencies by introducing a dataset with measurements from four real rooms. The ISOBEL Sound Field dataset is publicly available, and aims to bridge the gap between synthetic and real-world sound fields in rectangular rooms. Moreover, the paper advances on a recent deep learning-based method for sound field reconstruction using a very low number of microphones, and proposes an approach for modeling both magnitude and phase response in a U-Net-like neural network architecture. The complex-valued sound field reconstruction demonstrates that the estimated room transfer functions are of high enough accuracy to allow for personalized sound zones with contrast ratios comparable to ideal room transfer functions using 15 microphones below 150 Hz.
翻译:扩音器反应知识知识在一些应用中是有用的,因为声音系统位于一个根据会议室内位置改变听觉经验的房间里。为位于回声室内的音源获取音频场,可以通过对房间内脉冲反应功能进行劳动密集型测量,或者通过重建方法(可能需要大大降低测量量)来实现。本文件通过采用四个真实房间的测量数据组,扩展对低频率实地健康重建的评估。ISOBEL声音场数据集是公开的,目的是弥合孔形室合成和真实世界音域之间的差距。此外,最近采用非常低的麦克风对基于深深学习的实地健康重建方法,提出了在U-Net型神经网络结构中模拟规模和阶段反应的方法。经过复杂估价的声场重建表明,估计的房间转移功能足够精确,足以使个人化音区能够使用与理想室转移功能相比的比值,使用低于150赫兹的15个麦克风进行对比。