Robust and efficient multiple alignment of unsynchronized meeting recordings

TLDR: This paper proposes a way to generate a single high-quality audio recording of a meeting using no equipment other than participants' personal devices using an adaptive audio fingerprint based on spectrotemporal eigenfilters, where the fingerprint design is learned on-the-fly in a totally unsupervised way to perform well on the data at hand.

Abstract: This paper proposes a way to generate a single high-quality audio recording of a meeting using no equipment other than participants’ personal devices. Each participant in the meeting uses their mobile device as a local recording node, and they begin recording whenever they arrive in an unsynchronized fashion. The main problem in generating a single summary recording is to temporally align the various audio recordings in a robust and efficient manner. We propose a way to do this using an adaptive audio fingerprint based on spectrotemporal eigenfilters, where the fingerprint design is learned on-the-fly in a totally unsupervised way to perform well on the data at hand. The adaptive fingerprints require only a few seconds of data to learn a robust design, and they require no tuning. Our method uses an iterative, greedy two-stage alignment algorithm which finds a rough alignment using indexing techniques, and then performs a more fine-grained alignment based on Hamming distance. Our proposed system achieves $>$ 99% alignment accuracy on challenging alignment scenarios extracted from the ICSI meeting corpus, and it outperforms five other well-known and state-of-the-art fingerprint designs. We conduct extensive analyses of the factors that affect the robustness of the adaptive fingerprints, and we provide a simple heuristic that can be used to adjust the fingerprint’s robustness according to the amount of computation we are willing to perform.