Experiments on the DCASE Challenge 2016: Acoustic Scene Classification and Sound Event Detection in Real Life Recording
This work addresses the problem of improving automated audio analysis for applications like surveillance or smart devices, but it appears incremental as it builds on existing DCASE challenge baselines with optimizations.
The paper tackled acoustic scene classification and sound event detection in real-life recordings, achieving an overall accuracy of 78.9% (vs. baseline 72.6%) for Task 1 and a segment-based error rate of 0.76 (vs. baseline 0.91) for Task 3.
In this paper we present our work on Task 1 Acoustic Scene Classi- fication and Task 3 Sound Event Detection in Real Life Recordings. Among our experiments we have low-level and high-level features, classifier optimization and other heuristics specific to each task. Our performance for both tasks improved the baseline from DCASE: for Task 1 we achieved an overall accuracy of 78.9% compared to the baseline of 72.6% and for Task 3 we achieved a Segment-Based Error Rate of 0.76 compared to the baseline of 0.91.