Nowadays, monitoring of people and events is a common matter in the street, in the industry or at home, and acoustic event detection is commonly used. This increases the knowledge of what is happening in the soundscape, and this information encourages any monitoring system to take decisions depending on the measured events. Our research in this field includes, on one hand, smart city applications, which aim is to develop a low cost sensor network for real time noise mapping in the cities, and on the other hand, ambient assisted living applications through audio event recognition at home. This requires acoustic signal processing for event recognition, which is a challenging problem applying feature extraction techniques and machine learning methods. Furthermore, when the techniques come closer to implementation, a complete study of the most suitable platform is needed, taking into account computational complexity of the algorithms and commercial platforms price. In this work, the comparative study of several platforms serving to implement this sensing application is detailed. An FPGA platform is chosen as the optimum proposal considering the application requirements and taking into account time restrictions of the signal processing algorithms. Furthermore, we describe the first approach to the real-time implementation of the feature extraction algorithm on the chosen platform.