No abstract

In this paper, we address the problem of dual-microphone speech reinforcement for improving in-car speech communication via howling control. A speech reinforcement system acquires speech from a speaker’s microphone and delivers it to the other listeners in the car cabin through loudspeakers. A car cabin’s small space makes it vulnerable to acoustic feedback, resulting in the appearance of howling noises. The proposed system aims to maintain a desired high amplification gain over time while not compromising the output speech quality. The dual-microphone system consists of a microphone for speech acquisition and another microphone that monitors the environment for howling detection, where its location depends on its howling detection sensitivity. The proposed algorithm contains a gain-control segment based on the magnitude-slope-deviation measure, which reduces the amplification-gain in the case of howling detection. To find the optimal locations of the howling-detection microphone in the cabin, for a devised set of scenarios, a Pareto optimization method is applied. The Pareto optimization considers the bi-objective nature of the problem, i.e., minimizing both the relative gain-reduction and the overall speech distortion. It is shown that the proposed dual-microphone system outperforms a single-microphone-based system. The performance improvement is demonstrated by showing the higher howling detection sensitivity of the dual-microphone system. Additionally, a microphone constellation design process, for optimal howling detection, is provided through the utilization of the Pareto fronts and anti-fronts approach.

In this paper, we address the problem of howling detection in speech reinforcement system applications for utilization in howling control mechanisms. A general speech reinforcement system acquires speech from a speaker’s microphone, and delivers a reinforced speech to other listeners in the same room, or another room, through loudspeakers. The amount of gain that can be applied to the acquired speech in the closed-loop system is constrained by electro-acoustic coupling in the system, manifested in howling noises appearing as a result of acoustic feedback. A howling detection algorithm aims to early detect frequency-howls in the system, before the human ear notices. The proposed algorithm includes two cascaded stages: Soft Howling Detection and Howling False-Alarm Detection. The Soft Howling Detection is based on the temporal magnitude-slope-deviation measure, identifying potential candidate frequency-howls. Inspired by the temporal approach, the Howling False-Alarm Detection stage considers the understanding of speech-signal frequency components’ magnitude behavior under different levels of acoustic feedback. A comprehensive howling detection performance evaluation process is designed, examining the proposed algorithm in terms of detection accuracy and the time it takes for detection, under a devised set of howling scenarios. The performance improvement of the proposed algorithm, with respect to a plain magnitude-slope-deviation-based method, is demonstrated by showing faster detection response times over a set of howling change-rate configurations. The two-staged proposed algorithm also provides a significant recall improvement, while improving the precision decrease via the Howling False-Alarm Detection stage.