# Deep Learning-Based Portable Device for Audio Distress Signal Recognition in Urban Areas
###### Abstract  
Real-time automatic identification of audio distress signals in urban areas is a task that in a smart city can improve response times in emergency alert systems. The main challenge in this problem lies in finding a model that is able to accurately recognize these type of signals in the presence of background noise and allows for real-time processing. In this paper, we present the design of a portable and low-cost device for accurate audio distress signal recognition in real urban scenarios based on deep learning models. As real audio distress recordings in urban areas have not been collected and made publicly available so far, we first constructed a database where audios were recorded in urban areas using a low-cost microphone. Using this database, we trained a deep multi-headed 2D convolutional neural network that processed temporal and frequency features to accurately recognize audio distress signals in noisy environments with a significant performance improvement to other methods from the literature. Then, we deployed and assessed the trained convolutional neural network model on a Raspberry Pi that, along with the low-cost microphone, constituted a device for accurate real-time audio recognition. Source code and database are publicly available. acoustic signal processing; smart cities; convolutional neural network; raspberry Pi; deep learning +
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urban areas. These developments are mostly focused on detection via visual input rather than audio ones because, in public places, background noise is a concern and the detection of events that overlap with noise can be a challenging task. Moreover, the distance of the sound source to the microphone can make it difficult to detect certain events. Examples of machine learning surveillance systems based on visual input involve detecting abnormal behavior through action recognition which aims to distinguish activities such as violence identification [2], drowning identification [3], and atypical human falling detection [4]. Very little has been done to develop automatic systems for the recognition of audio distress signals in open urban areas. In this context, audio distress signals refer to emergency shouts coming from distressed people in the streets that can be either just a shout that contains no speech or a phrase in shouted form such as \"help me!\" The automatic detection of audio distress signals is a challenging task because these systems have to recognize both speech and emotion, and discriminate between a normal street sound, such as a salesman shouting offers in the street, and an actual emergency shout. Multiple databases that collect vocal distress signals have been constructed and tested using standard recognition models. For example, the database presented in [5] includes audios with utterances of emotional speech. Moreover, the databases presented in [6,7] include audios that are labeled with different types of emotions and recorded in low noise environments as houses or closed locations. The work in [8] presented a large database that includes streaming sounds that were artificially overlapped with white additive Gaussian noise. These databases have been useful to study the development of models for the automatic recognition of audio distress signals, and have been used to validate recognition models for sentiment analysis, as presented in [9, 10, 11, 12]. Audios in these databases are typically recorded in low noise environments using expensive high-quality microphones, limiting the reach of real implementations. In this paper, we propose a device implementation that locally uses deep convolutional neural networks to detect and classify different audio distress signals in real urban areas. Such devices can potentially improve response times in emergency alert systems in smart cities. Our contribution to this subject is twofold: (i) a device that is designed to accurately conduct the recognition task in urban areas where generally loud noise is a concern, and (ii) a device that is portable and low cost.