Optimization of low frequency sound reproduction in enclosed space

Abstract

Achieving accurate low-frequency reproduction in enclosed spaces is notoriously difficult, as standing waves and modal resonances introduce significant peaks, dips, and extended decay times in the bass range. This thesis presents a multi-loudspeaker corrective strategy that employs adaptive filtering to mitigate these issues, focusing on the 20–100 Hz region where modal effects are most pronounced. By iteratively refining digital filters using a Least Mean Squares (LMS) algorithm, each loudspeaker compensates for problematic resonances, thereby improving the overall frequency response and reducing spatial variability. The methodology is validated through both virtual and real-world evaluations. In simulations based on modal summation, the proposed approach demonstrates substantial reductions in modal peaks and ringing, establishing an idealized benchmark. Subsequent experiments in an actual listening environment confirm that LMS-based filters effectively smooth out low-frequency irregularities caused by the primary loudspeaker, although the integration of multiple sources highlights challenges related to phase alignment, overlapping modes, and precise calibration requirements. Despite these complexities, the results underscore the viability of multi-loudspeaker adaptive filtering for localized modal compensation, pointing to future avenues such as fully joint multi-channel optimization and machine learning–based filter tuning. Overall, this work advances the development of adaptive room correction systems by illustrating both the benefits and constraints of a self-optimizing, low-frequency–focused design.