Reducing computational complexity in adaptive sound zones with online room impulse response estimation

Sound zone techniques allow processing audio signals to control a set of loudspeakers and playback independent audio content in specific areas in a room, typically sampled through a microphone array. This task comprises two processes: acquiring room impulse responses (RIRs) between all loudspeakers and microphones and, based on these, calculating the control filters. Recent adaptive filtering methods allow performing both processes simultaneously, resulting in sound zones able to adapt to changes in the system. However, existing sample-based implementations, processing one input sample per iteration, are computationally very expensive. Alternatively, a block-based implementation is proposed, which, processing several samples per iteration, allows reducing the computational demands of such dynamic sound zones. Further reductions are achieved by truncating the RIR estimates and using less computationally demanding adaptive filter update algorithms. With respect to sample-based approaches, the proposed block-based processing can reduce the computational complexity by more than 90%, in that case, at the expense of increasing the time required to reach a certain acoustic performance. Furthermore, the proposed block-based scheme successfully adapts to changes, and inaccurate RIR estimations do not hinder sound zones rendering. The method was experimentally validated, and further reductions in the computational complexity can be made through frequency domain implementations.