  Machine learning approaches to auditory object recognition are traditionally
based on engineered features such as those derived from the spectrum or
cepstrum. More recently, end-to-end classification systems in image and
auditory recognition systems have been developed to learn features jointly with
classification and result in improved classification accuracy. In this paper,
we propose a novel end-to-end deep neural network to map the raw waveform
inputs to sound class labels. Our network includes an "inception nucleus" that
optimizes the size of convolutional filters on the fly that results in reducing
engineering efforts dramatically. Classification results compared favorably
against current state-of-the-art approaches, besting them by 10.4 percentage
points on the Urbansound8k dataset. Analyses of learned representations
revealed that filters in the earlier hidden layers learned wavelet-like
transforms to extract features that were informative for classification.
