Audio Surveillance

Abstract

Despite surveillance systems becoming increasingly ubiquitous in our living environment, automated surveillance, currently based on video sensory modality and machine intelligence, lacks most of the time the robustness and reliability required in several real applications. To tackle this issue, audio sensory devices have been incorporated, both alone or in combination with video, giving birth in the past decade, to a considerable amount of research. In this article, audio-based automated surveillance methods are organized into a comprehensive survey: A general taxonomy, inspired by the more widespread video surveillance field, is proposed to systematically describe the methods covering background subtraction, event classification, object tracking, and situation analysis. For each of these tasks, all the significant works are reviewed, detailing their pros and cons and the context for which they have been proposed. Moreover, a specific section is devoted to audio features, discussing their expressiveness and their employment in the above-described tasks. Differing from other surveys on audio processing and analysis, the present one is specifically targeted to automated surveillance, highlighting the target applications of each described method and providing the reader with a systematic and schematic view useful for retrieving the most suited algorithms for each specific requirement.

Learning representations of sound using trainable COPE feature extractors

“…In this paper we focus on automatic learning of representations of sounds that are suitable for pattern recognition, in the context of environmental sound analysis for detection and classification of audio events. Recently, the interest in automatic analysis of environmental sounds increased because of various applications in intelligent surveillance and security [1], assistance of eldery people [2], monitoring of smart rooms [3], home and social robotics [4], etc.…”

Joint Optimization of Deep Neural Network-Based Dereverberation and Beamforming for Sound Event Detection in Multi-Channel Environments

“…Sound event detection (SED) is desired as a task that detects the onset and offset times for each sound event in an audio segment. Various sounds always occur around us, and SED enables many services, including social care [1], audio surveillance [2,3], drone detection [4], and bird detection [5], by allowing machines to recognize sound events like the human auditory system. In recent years, the Detection and Classification of Acoustic Scenes and Events (DCASE) challenge and deep learning have significantly accelerated the research of SED.…”

Sound Event Detection Using Derivative Features in Deep Neural Networks

“…Among these tasks, sound event detection (SED) is aimed at identifying both the existence and occurrence times of the various sounds in our daily lives [1]. It has several applications, such as surveillance [2,3], urban sound analysis [4], information retrieval from multimedia content [5], health care monitoring [6], and bird call detection [7].…”