  Acoustic scene classification is a process of characterizing and classifying
the environments from sound recordings. The first step is to generate features
(representations) from the recorded sound and then classify the background
environments. However, different kinds of representations have dramatic effects
on the accuracy of the classification. In this paper, we explored the three
such representations on classification accuracy using neural networks. We
investigated the spectrograms, MFCCs, and embeddings representations using
different CNN networks and autoencoders. Our dataset consists of sounds from
three settings of indoors and outdoors environments - thus the dataset contains
sound from six different kinds of environments. We found that the spectrogram
representation has the highest classification accuracy while MFCC has the
lowest classification accuracy. We reported our findings, insights as well as
some guidelines to achieve better accuracy for environment classification using
sounds.
