On the Integration of Acoustics and LiDAR: a Multi-Modal Approach to Acoustic Reflector Estimation

Having knowledge on the room acoustic properties, e.g., the location of acoustic reflectors, allows to better reproduce the sound field as intended. Current state-of-the-art methods for room boundary detection using microphone measurements typically focus on a two-dimensional setting, causing a model mismatch when employed in real-life scenarios. Detection of arbitrary reflectors in three dimensions encounters practical limitations, e.g., the need for a spherical array and the increased computational complexity. Moreover, loudspeakers may not have an omnidirectional directivity pattern, as usually assumed in the literature, making the detection of acoustic reflectors in some directions more challenging. In the proposed method, a LiDAR sensor is added to a loudspeaker to improve wall detection accuracy and robustness. This is done in two ways. First, the model mismatch introduced by horizontal reflectors can be resolved by detecting reflectors with the LiDAR sensor to enable elimination of their detrimental influence from the 2D problem in pre-processing. Second, a LiDAR-based method is proposed to compensate for the challenging directions where the directive loudspeaker emits little energy. We show via simulations that this multi-modal approach, i.e., combining microphone and LiDAR sensors, improves the robustness and accuracy of wall detection.

I. Introduction

Room properties have a large influence on the sound field reproduction, meaning the highest reproduction quality can only be obtained when the room influence is known. For example, when a loudspeaker is placed close to a wall or corner, the lower frequency range in the room is amplified compared to the mid and high frequency, resulting in an unbalanced sound experience [1]. Working from home has led to a large increase in teleconference meetings. To use teleconference meetings as a feasible alternative to in-person meetings, speech intelligibility is a crucial factor. The reduced speech intelligibility due to echoes introduced by the room, makes it crucial to be aware of the nearby walls that introduce these echoes [2]. The introduction of smart loudspeakers gives rise to opportunities to estimate room parameters to improve the sound experience of the user. Of particular importance is information on the proximity of the walls. If the wall locations are known, their effects can be compensated for using digital filters [3] [4]. In this paper we address the problem of detecting the walls in close proximity to the speaker using sensors on the speaker.