Automated learning of playout scheduling algorithms for improving perceptual conversational quality in multi-party VoIP

In this paper, we propose four methods for equalizing the silence periods experienced by users in a multi-party VoIP conversation in order to improve their perceived conversational quality. To mitigate the unbalanced silence periods caused by delay disparities in Internet connections, the playout scheduler at the receiver of each client equalizes the silence periods experienced. Our limited subjective tests show that we can improve the perceptual quality when the network connections are lossy and have large delay disparities. Because it is impossible to conduct subjective tests under all possible conditions, we have developed a classifier that learns to select the best equalization algorithm using learning examples derived from subjective tests under limited network and conversational conditions. Our experimental results show that our classifier can consistently pick the best algorithm with the highest subjective conversational quality under unseen conditions, and that our system has better perceptual quality when compared to that of Skype (Version 3.6.0.244).