# _Applied_
###### Abstract  
Environmental Sound Recognition has become a relevant application for smart cities. Such an application, however, demands the use of trained machine learning classifiers in order to categorize a limited set of audio categories. Although classical machine learning solutions have been proposed in the past, most of the latest solutions that have been proposed toward automated and accurate sound classification are based on a deep learning approach. Deep learning models tend to be large, which can be problematic when considering that sound classifiers often have to be embedded in resource constrained devices. In this paper, a classical machine learning based classifier called MosAIc, and a lighter Convolutional Neural Network model for environmental sound recognition, are proposed to directly compete in terms of accuracy with the latest deep learning solutions. Both approaches are evaluated in an embedded system in order to identify the key parameters when placing such applications on constrained devices. The experimental results show that classical machine learning classifiers can be combined to achieve similar results to deep learning models, and even outperform them in accuracy. The cost, however, is a larger classification time. machine learning; environment sound recognition; Convolutional Neural Network; embedded system; audio feature extraction; multi-class classification; one-vs-all +
Footnote †: journal: Applied Sciences  
crops [5]. Repelling birds is not only important to save crops, but also for saving the lives of the birds. Although wind farms are a green source of electricity, they have a negative impact on bird populations. Research estimates that up to half a million birds do not survive a collision with a wind turbine every year in the U.S. alone [6]. An ESR system near the wind farm can activate a collision avoidance procedure when a bird is too close to prevent a collision. Although the focus of this work is on urban sound, some of the datasets also contain sounds that are not related to urban environments, yet, they are relevant for other ESR applications. ESR is often performed on embedded systems, which are deployed in the area being monitored to perform audio recognition. Such systems provide limited computational power and memory capacity. Moreover, they are in general battery powered, which limits the use of complex algorithms, which can drastically reduce the autonomy of the system. Although recent works have proposed solutions [7], these limitations have slowed the deployment of ESR on embedded devices. Deep Learning (DL) approaches have been proposed in recent years [8] which outperform the existing classical Machine Learning (ML) solutions in the field of Artificial Intelligence (AI). However, DL models have been growing in complexity, like in number of parameters, in order to deliver higher accuracy, leading to a higher inference time. The authors believe that classical ML based alternatives, which present a lower complexity, and therefore, a lower execution time, can still be competitive against DL models for ESR. This is especially interesting considering that they are used in constrained embedded devices. The presented work exploits classical ML by combining different well-known ML classifiers, which is called Mesh of Sound AI-based Classifiers or MoSAIc for short. This MoSAIc approach is the ultimate attempt to exploit classical ML techniques to achieve similar or better accuracies than when using DL. Together with this novel approach, a lightweight DL approach is also proposed to overcome the limitations of embedded devices. Both approaches are compared and evaluated in different platforms, including a normal PC and a popular embedded device. Several audio datasets used in the related literature are used in order to provide a fair comparison and evaluation. Parameters such as accuracy and execution time are used as the metrics in evaluation.