  Recent successful applications of convolutional neural networks (CNNs) to
audio classification and speech recognition have motivated the search for
better input representations for more efficient training. Visual displays of an
audio signal, through various time-frequency representations such as
spectrograms offer a rich representation of the temporal and spectral structure
of the original signal. In this letter, we compare various popular signal
processing methods to obtain this representation, such as short-time Fourier
transform (STFT) with linear and Mel scales, constant-Q transform (CQT) and
continuous Wavelet transform (CWT), and assess their impact on the
classification performance of two environmental sound datasets using CNNs. This
study supports the hypothesis that time-frequency representations are valuable
in learning useful features for sound classification. Moreover, the actual
transformation used is shown to impact the classification accuracy, with
Mel-scaled STFT outperforming the other discussed methods slightly and baseline
MFCC features to a large degree. Additionally, we observe that the optimal
window size during transformation is dependent on the characteristics of the
audio signal and architecturally, 2D convolution yielded better results in most
cases compared to 1D.
