  Deep neural network architectures designed for application domains other than
sound, especially image recognition, may not optimally harness the
time-frequency representation when adapted to the sound recognition problem. In
this work, we explore the ConditionaL Neural Network (CLNN) and the Masked
ConditionaL Neural Network (MCLNN) for multi-dimensional temporal signal
recognition. The CLNN considers the inter-frame relationship, and the MCLNN
enforces a systematic sparseness over the network's links to enable learning in
frequency bands rather than bins allowing the network to be frequency shift
invariant mimicking a filterbank. The mask also allows considering several
combinations of features concurrently, which is usually handcrafted through
exhaustive manual search. We applied the MCLNN to the environmental sound
recognition problem using the ESC-10 and ESC-50 datasets. MCLNN achieved
competitive performance, using 12% of the parameters and without augmentation,
compared to state-of-the-art Convolutional Neural Networks.
