Audio Data Mining for Anthropogenic Disaster Identification: An Automatic Taxonomy Approach

Abstract

Disasters are undesirable and often sudden events causing human, material and economic losses, which exceed the coping capability of the affected community or society. In recent years, with significant advancement in information technology, various intelligent systems have been developed to support various aspects of disaster management, including emergency prediction, timely response and aftermath recovery. This paper addresses the anthropogenic disaster identification issue by exploiting ambient sound data. Specifically, a novel and efficient acoustic event classification scheme is proposed, which is based on unsupervised acoustic feature learning and data-driven taxonomy. The proposed framework could accurately identify anthropogenic disaster events, e.g., gun shot, explosion, scream cry, etc. from dynamic audio data. and it consists of three major stages as follows. First, predominant acoustic patterns are characterized by dictionary learning algorithms, which can generate robust acoustic feature representations for recognition under noisy conditions. Second, hazard sound event taxonomy is created by exploiting probabilistic distances between extracted class-wise dictionaries. Finally, taxonomy structure is embedded into hierarchical classification algorithm to improve event identification performance. The Proposed approach is evaluated using real-world dataset with 10 emergency sound categories and 3,275 clips. According to extensive experimental comparisons, proposed approach achieved state-of-the-art performance in anthropogenic disaster identification.