  Audio-based multimedia retrieval tasks may identify semantic information in
audio streams, i.e., audio concepts (such as music, laughter, or a revving
engine). Conventional Gaussian-Mixture-Models have had some success in
classifying a reduced set of audio concepts. However, multi-class
classification can benefit from context window analysis and the discriminating
power of deeper architectures. Although deep learning has shown promise in
various applications such as speech and object recognition, it has not yet met
the expectations for other fields such as audio concept classification. This
paper explores, for the first time, the potential of deep learning in
classifying audio concepts on User-Generated Content videos. The proposed
system is comprised of two cascaded neural networks in a hierarchical
configuration to analyze the short- and long-term context information. Our
system outperforms a GMM approach by a relative 54%, a Neural Network by 33%,
and a Deep Neural Network by 12% on the TRECVID-MED database
