Measured spatial room impulse responses have been used to compare acoustic spaces. One way to analyze and render such responses is to apply parametric methods, yet those methods have been bound to single measurement locations. This paper introduces a method that locates image sources from spatial room impulse responses measured at multiple source and receiver positions. The method aligns the measurements to a common coordinate frame and groups stable direction-of-arrival estimates to find image source positions. The performance of the method is validated with three case studies—one small room and two concert halls. The studies show that the method is able to locate the most prominent image sources even in complex spaces, providing new insights into available Spatial Room Impulse Response (SRIR) data and a starting point for six degrees of freedom (6DoF) acoustic rendering.

Abstract:The use of headphones in reproducing spatial sound is becoming more and more popular. For instance, virtual reality applications often use head-tracking to keep the binaurally reproduced auditory environment stable and to improve externalization. Here, we study one spatial sound reproduction method over headphones, in particular the positioning of the virtual loudspeakers. The paper presents an algorithm that optimizes the positioning of virtual reproduction loudspeakers to reduce the computational cost in head-tracked real-time rendering. The listening test results suggest that listeners could discriminate the optimized loudspeaker arrays for renderings that reproduced a relatively simple acoustic conditions, but optimized array was not significantly different from equally spaced array for a reproduction of a more complex case. Moreover, the optimization seems to change the perceived openness and timbre, according to the verbal feedback of the test subjects.

There are a lot of colleagues that I grateful for the support and companionship. I thank Professor Jaakko Lehtinen for nudging me towards the academy and showing what can be achieved by thinking big; Tapani Pihlajakuja for breathing new life to my PhD; and Nils Meyer-Kahlen for reaching completely new heights of invention. I also thank coauthors Jukka Pätynen for introduction to research work and Raimundo Gonzalez for intriguing journeys inside and outside office hours. I am also grateful to all the colleagues and partners that helped me along the way: Benoit

Image source reversion algorithms estimate room geometry from measured spatial room impulse responses by locating image sources. However, most of the methods have been limited to a single loudspeaker position and to convex rooms. Earlier, we have proposed a method that combines image sources from multiple receiver locations to find more image sources accurately even in concave rooms. Here, we extend the method to cope with multiple sound sources, thus the image source search can utilize measurements from multiple source and receiver positions simultaneously. The search method is tested with two measurement datasets and is found to improve the result compared to the previous algorithm. For the future applications, we also propose methods for generating a concave room model from the found reflection planes, for estimating material filters for each surface, and for interpolating between measured source locations.