Exploiting Balcony Sound Atmospheres for Automatic Prediction of Floors with a Voted-Majority Approach Based on Neural Networks

This paper tackles the challenging task of automatically predicting the floor of a balcony based on a sound bound of two minutes recorded on that balcony. The sound fragments are typical of the environment, as nothing out of the usual can be heard in them. However, there is a good probability that, when hearing a fragment in quiet surroundings with consistent metropolitan background noise, it would not be straightforward to determine or estimate the floor height. In our experiments, it was found that sound chunks lasting 5 s can be identified with high accuracy even with a small number of training samples. In addition, when using a late fusion strategy to combine the outputs of classifiers trained on two modalities of the sound tracks, the floors of these bands are perfectly correctly classified. This result was consistent throughout all twenty tests when training and test sets were chosen at random, supporting the viability of the suggested method.