  Automatic feature extraction using neural networks has accomplished
remarkable success for images, but for sound recognition, these models are
usually modified to fit the nature of the multi-dimensional temporal
representation of the audio signal in spectrograms. This may not efficiently
harness the time-frequency representation of the signal. The ConditionaL Neural
Network (CLNN) takes into consideration the interrelation between the temporal
frames, and the Masked ConditionaL Neural Network (MCLNN) extends upon the CLNN
by forcing a systematic sparseness over the network's weights using a binary
mask. The masking allows the network to learn about frequency bands rather than
bins, mimicking a filterbank used in signal transformations such as MFCC.
Additionally, the Mask is designed to consider various combinations of
features, which automates the feature hand-crafting process. We applied the
MCLNN for the Environmental Sound Recognition problem using the Urbansound8k,
YorNoise, ESC-10 and ESC-50 datasets. The MCLNN have achieved competitive
performance compared to state-of-the-art Convolutional Neural Networks and
hand-crafted attempts.
