Experiments on the DCASE Challenge 2016: Acoustic Scene Classification and Sound Event Detection in Real Life Recording

TL;DR

This paper reports on experiments for acoustic scene classification and sound event detection in real-life recordings, achieving significant improvements over baseline performance through feature and classifier optimization.

Contribution

The authors demonstrate enhanced methods for acoustic scene classification and sound event detection, surpassing baseline results in the DCASE 2016 challenge.

Findings

Achieved 78.9% accuracy in scene classification

Reduced segment-based error rate to 0.76 in sound event detection

Implemented feature and classifier optimizations

Abstract

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.