Deep Feature Learning for Acoustics-Based Terrain Classification

Abstract

In order for robots to efficiently navigate in real-world environments, they need to be able to classify and characterize terrain for safe navigation. The majority of techniques for terrain classification is predominantly based on using visual features. However, as vision-based approaches are severely affected by appearance variations and occlusions, relying solely on them incapacitates the ability to function robustly in all conditions. In this paper, we propose an approach that uses sound from vehicle-terrain interactions for terrain classification. We present a new convolutional neural network architecture that learns deep features from spectrograms of extensive audio signals, gathered from interactions with various indoor and outdoor terrains. Using exhaustive experiments, we demonstrate that our network significantly outperforms classification approaches using traditional audio features by achieving state of the art performance. Additional experiments reveal the robustness of the network in situations corrupted with varying amounts of white Gaussian noise and that fine-tuning with noise-augmented samples significantly boosts the classification rate. Furthermore, we demonstrate that our network performs exceptionally well even with samples recorded with a low-quality mobile phone microphone that adds substantial amount of environmental noise.