Water flow detection from a wearable device with a new feature, the spectral cover

This paper presents a new system for water flow detection on real life recordings and its application to medical context. The recognition system is based on an original feature for sound event detection in real life. This feature, called ”spectral cover” shows an interesting behaviour to recognize water flow in a noisy environment. The system is only based on thresholds. It is simple, robust, and can be used on every corpus without training. An experiment is realized with more than 7 hours of videos recorded by a wearable device. Our system obtains good results for the water flow event recognition (F-measure of 66%). A comparison with classical approaches using MFCC or low levels descriptors with GMM classifiers is done to attest the good performance of our system. Adding the spectral cover to low levels descriptors also improve their performance and confirms that this feature is relevant.