CNN-RNN network for impulsive urban sound detection in stream of audio signals

Abstract

In the modern urban milieu, the ambient soundscape plays a pivotal role in shaping the perceptions and experiences of city-dwellers. From the incessant hum of traffic to the sporadic clatter of construction, the auditory landscape of cities provides a rich tapestry of information. Within this soundscape, impulsive urban sounds—those abrupt, transient noises that punctuate the continuous din—carry particular significance. They can be indicative of unforeseen events, such as vehicular accidents, machinery malfunctions, or other anomalous occurrences that require swift attention. Consequently, the accurate and timely detection of these impulsive sounds becomes crucial for various applications ranging from urban planning and environmental monitoring to emergency response and public safety.

Traditional methods for sound detection in urban settings have largely relied on threshold-based techniques or manual monitoring. However, these methods are often plagued by high false positive rates, especially in the context of the dense and overlapping auditory stimuli of urban environments. Additionally, manual monitoring is not scalable and is subject to human error. As urban centers grow and evolve, there is a pressing need for more sophisticated, automated techniques that can adeptly navigate the complexities of the urban soundscape.

The advent of deep learning and neural networks has ushered in new possibilities for audio signal processing. Specifically, Convolutional Neural Networks (CNNs) have demonstrated remarkable prowess in recognizing patterns and hierarchies in spatial data, while Recurrent Neural Networks (RNNs) have showcased their strength in processing sequences and temporal information. Considering the spatial-temporal nature of audio signals, a synergy of CNNs and RNNs presents an intriguing proposition. This research paper delves into the potential of a hybrid CNN-RNN architecture tailored for the detection of impulsive urban sounds within continuous audio streams, aiming to provide a robust solution that addresses the limitations of traditional methods and harnesses the capabilities of modern neural network architectures.