Recognition of blowing sound types for real-time implementation in mobile devices

This paper presents a system to recognize and classify sounds produced by human subjects blowing air by the mouth. The objective is to implement the system for fast recognition using low-complexity algorithms in a low-budget processor. Recognition is achieved using tailored band energy ratios, modified frequency centroid and a periodicity test based on spectrum autocorrelation. These lightweight feature extraction techniques are adapted to the particular task of recognition of blowing sound types. The classification is achieved by a naive Bayes classifier. The algorithm can be implemented in real-time (latency ≤ 100 ms) and experimental test results show average recognition rates over 94 %.