Exploring superframe co-occurrence for acoustic event recognition

We introduce in this paper a concept of using acoustic superframes, a mid-level representation which can overcome the drawbacks of both global and simple frame-level representations for acoustic events. Through superframe-level recognition, we explore the phenomenon of superframe co-occurrence across different event categories and propose an efficient classification scheme that takes advantage of this feature sharing to improve the event-wise recognition power. We empirically show that our recognition system results in 2.7% classification error rate on the ITC-Irst database. This state-of-the-art performance demonstrates the efficiency of this proposed approach. Furthermore, we argue that this presentation can pretty much facilitate the event detection task compared to its counterparts, e.g. global and simple frame-level representations.