  We present the ConditionaL Neural Network (CLNN) and the Masked ConditionaL
Neural Network (MCLNN) designed for temporal signal recognition. The CLNN takes
into consideration the temporal nature of the sound signal and the MCLNN
extends upon the CLNN through a binary mask to preserve the spatial locality of
the features and allows an automated exploration of the features combination
analogous to hand-crafting the most relevant features for the recognition task.
MCLNN has achieved competitive recognition accuracies on the GTZAN and the
ISMIR2004 music datasets that surpass several state-of-the-art neural network
based architectures and hand-crafted methods applied on both datasets.
