Classification of Building Noise Type/Position via Supervised Learning

Hwiyong Choi, Haesang Yang, Seungjun Lee, Woojae Seong

Abstract: This paper presents noise type/position classification of various impact noises generated in a building which is a serious conflict issue in apartment complexes. For this study, a collection of floor impact 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 impact 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.

Keywords: impact noise, noise type classification, noise position classification, convolutional neural networks, transfer learning

TL;DR: This paper presents noise type/position classification of various impact noises generated in a building which is a serious conflict issue in apartment complexes