Environmental Audio Scene and Sound Event Recognition for Autonomous Surveillance

Abstract

Monitoring of human and social activities is becoming increasingly pervasive in our living environment for public security and safety applications. The recognition of suspicious events is important in both indoor and outdoor environments, such as child-care centers, smart-homes, old-age homes, residential areas, office environments, elevators, and smart cities. Environmental audio scene and sound event recognition are the fundamental tasks involved in many audio surveillance applications. Although numerous approaches have been proposed, robust environmental audio surveillance remains a huge challenge due to various reasons, such as various types of overlapping audio sounds, background noises, and lack of universal and multi-modal datasets. The goal of this article is to review various features of representing audio scenes and sound events and provide appropriate machine learning algorithms for audio surveillance tasks. Benchmark datasets are categorized based on the real-world scenarios of audio surveillance applications. To have a quantitative understanding, some of the state-of-the-art approaches are evaluated based on two benchmark datasets for audio scenes and sound event recognition tasks. Finally, we outline the possible future directions for improving the recognition of environmental audio scenes and sound events.

Environmental Acoustics Modelling Techniques for Forest Monitoring

“…EASR refers to recognition of indoor or outdoor acoustic scenes (e.g., cafes/restaurants, home, vehicle or metro stations, supermarkets, versus crowded or silent streets, forest landscape, countryside, beaches, gym halls, swimming pools). SER is intended to the investigation of specific acoustic events in the audio environments, like dog barking, gunshots, sudden brake sounds, or human nonspeech events, like coughing, whistling, screaming, child crying, snoring, sneezing [13].…”

A Comprehensive Review of Polyphonic Sound Event Detection

“…Rex [30] provided software recommendations for SED. Although a survey by Chandrakala and Jayalakshmi [31] included discussion of several SED systems, the survey was focused on the audio scene and event classification. The survey by Purwins et al [32] was mainly on the general overview of audio signal processing.…”

Sound Event Detection Utilizing Graph Laplacian Regularization with Event Co-Occurrence

“…Sound event detection (SED) is a task that identifies types of sound and detects their onset and offset [1]. Recently, many works have addressed SED because SED has a large potential for many applications such as monitoring elderly people or infants [2], [3], automatic surveillance [4]- [6], automatic anomaly detection [7], [8], and media retrieval [9]. SED is typically categorized into two types: monophonic and polyphonic SED.…”