Classification of inter-floor noise type/position via supervised learning

Abstract

This work presents noise type/position classification of various inter-floor noises generated in a building which is a serious conflict issue in apartment complexes. For this study, a collection of inter-floor noise dataset is recorded with a single microphone. Noise types/positions are selected based on a report by the Floor management Center under Korea Environmental Corporation. Using a convolutional neural networks based classifier, the inter-floor noise signals converted to log-scaled Mel-spectrograms are classified into noise types or positions. Also, our model is evaluated on a standard environmental sound dataset ESC-50 to show extensibility on environmental sound classification.