  One key step in audio signal processing is to transform the raw signal into
representations that are efficient for encoding the original information.
Traditionally, people transform the audio into spectral representations, as a
function of frequency, amplitude and phase transformation. In this work, we
take a purely data-driven approach to understand the temporal dynamics of audio
at the raw signal level. We maximize the information extracted from the raw
signal through a deep convolutional neural network (CNN) model. Our CNN model
is trained on the urbansound8k dataset. We discover that salient audio patterns
embedded in the raw waveforms can be efficiently extracted through a
combination of nonlinear filters learned by the CNN model.

Sound