  Neural network based architectures used for sound recognition are usually
adapted from other application domains such as image recognition, which may not
harness the time-frequency representation of a signal. The ConditionaL Neural
Networks (CLNN) and its extension the Masked ConditionaL Neural Networks
(MCLNN) are designed for multidimensional temporal signal recognition. The CLNN
is trained over a window of frames to preserve the inter-frame relation, and
the MCLNN enforces a systematic sparseness over the network's links that mimics
a filterbank-like behavior. The masking operation induces the network to learn
in frequency bands, which decreases the network susceptibility to
frequency-shifts in time-frequency representations. Additionally, the mask
allows an exploration of a range of feature combinations concurrently analogous
to the manual handcrafting of the optimum collection of features for a
recognition task. MCLNN have achieved competitive performance on the Ballroom
music dataset compared to several hand-crafted attempts and outperformed models
based on state-of-the-art Convolutional Neural Networks.
