  The ConditionaL Neural Networks (CLNN) and the Masked ConditionaL Neural
Networks (MCLNN) exploit the nature of multi-dimensional temporal signals. The
CLNN captures the conditional temporal influence between the frames in a window
and the mask in the MCLNN enforces a systematic sparseness that follows a
filterbank-like pattern over the network links. The mask induces the network to
learn about time-frequency representations in bands, allowing the network to
sustain frequency shifts. Additionally, the mask in the MCLNN automates the
exploration of a range of feature combinations, usually done through an
exhaustive manual search. We have evaluated the MCLNN performance using the
Ballroom and Homburg datasets of music genres. MCLNN has achieved accuracies
that are competitive to state-of-the-art handcrafted attempts in addition to
models based on Convolutional Neural Networks.
