A new paper introduces a sound field dataset, which is publicly available for development and evaluation of sound field reconstruction methods in four real rooms.
Knowledge of loudspeaker responses is useful in several applications, where a sound system is located inside a room that alters the listening experience depending on the position within the room. Acquisition of sound fields for sound sources located in reverberant rooms can be achieved through labour-intensive measurements of impulse response functions covering the room, or alternatively by means of reconstruction methods which can potentially require significantly fewer measurements.
A new paper submitted to the Journal of Acoustical Society of America 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 a recent deep learning-based method for sound field reconstruction using a very low number of microphones. It proposes an approach for modelling 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.
The paper is written by Miklas Strøm Kristoffersen1,2, Martin Bo Møller1, Pablo Martínez-Nuevo1 and Jan Østergaard2 from the Research Department, Bang & Olufsen and the Department of Electronic Systems, Aalborg University.
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