## 2 Related Work  
Several authors have compared classical ML techniques for sound classification for the evaluation of their ESR datasets [1; 2; 3]. However, a discussion about the convenience of embedding ML techniques for sound classification is missing.  
The work presented in [4] is one of the few describing an environmental sound classifier fully embedded on a wearable device. Their embedded system uses a 16-bit microcontroller (MCU) to perform a 1D Haar-like filtering for the feature extraction combined with a Hidden Markov Model (HMM) for the sound classification. Instead, this paper presents a more general evaluation of the most popular sound classifiers in terms of accuracy and response time.  
A Convolutional Neural Network (CNN) is proposed as a sound classifier in [5] for Wireless Sensor Networks (WSN) composed of Raspberry Pi acting as a node. The operations on the Raspberry Pi involve the encoding of the audio and its transmission to a computer, where the sound classification is performed. We believe that the feature extraction and the sound classification can be executed in such a relatively powerful embedded device. Our tests include the execution on a Raspberry Pi of all the operations required for the sound recognition.  
A distributed sensor network capable of urban sound recognition is presented in [6]. The authors propose a CNN-based classifier able to recognize 14 different types of urban sounds, which is embedded on a Raspberry Pi 3 acting as node of a distributed sensor network. Although all the operations seem to be embedded, no profiling of the execution time nor discussion about the achieved accuracy are provided.  
A centralized WSN for ESR is proposed in [7]. Their approach uses distributed nodes with minimal compute power for the audio data acquisition, compression and transmission to a central processing node (Raspberry Pi 3), where a Gaussian Mixture Model (GMM) performs the sound classification. An accuracy of 72% is achieved for only four categories of urban sounds. Unfortunately, the authors do not profile the embedded executions for the sound recognition or provide details of the overall latency of their centralized approach.  
The authors in [8] discuss the achievable accuracy and the required modifications to embed on a WSN node their Audio-Visual Correspondence (AVC) based architecture for urban sound recognition. Despite discussing the required modifications on their architecture, only the memory requirements are considered as the constraint parameter of the WSN node. The computational demand of the operations required for the urban sound recognition is not discussed for such contrained devices. Our evaluation covers not only the achievable accuracy, but also the time of response for each classifier.  
A system for monitoring, analysis and mitigation of urban noise pollution is proposed in [9; 10] as part of the SOunds of New York City (SONYC) project. A Raspberry Pi-based system acting as WSN nodes performs continuous Sound Pressure Level (SPL) measurements while acquiring, compressing and transmitting 10-second audio snippets at random intervals. Although the final objective is to fully embed the urban sound classification in the WSN node, the acquired audio is currently recognized on a laptop machine.  
To summarize, some authors have already evaluated different sound classifiers [1; 2; 3], but very few have effectively executed them on an embedded system [4; 6; 7]. Our evaluation not only covers a larger amount of sound classifiers but also profiles their execution on a state-of-the-art embedded system.