Urban Sound Classification using Long Short-Term Memory Neural Network
Iurii Lezhenin, Natalia Bogach, Evgeny Pyshkin · Annals of Computer Science and Information Systems · 2019
Environmental sound classification has received more attention in recent years.Analysis of environmental sounds is difficult because of its unstructured nature.However, the presence of strong spectro-temporal patterns makes the classification possible.Since LSTM neural networks are efficient at learning temporal dependencies we propose and examine a LSTM model for urban sound classification.The model is trained on magnitude mel-spectrograms extracted from UrbanSound8K dataset audio.The proposed network is evaluated using 5-fold cross-validation and compared with the baseline CNN.It is shown that the LSTM model outperforms a set of existing solutions and is more accurate and confident than the CNN.