  Neural network based architectures used for sound recognition are usually
adapted from other application domains, which may not harness sound related
properties. The ConditionaL Neural Network (CLNN) is designed to consider the
relational properties across frames in a temporal signal, and its extension the
Masked ConditionaL Neural Network (MCLNN) embeds a filterbank behavior within
the network, which enforces the network to learn in frequency bands rather than
bins. Additionally, it automates the exploration of different feature
combinations analogous to handcrafting the optimum combination of features for
a recognition task. We applied the MCLNN to the environmental sounds of the
ESC-10 dataset. The MCLNN achieved competitive accuracies compared to
state-of-the-art convolutional neural networks and hand-crafted attempts.
