  This paper proposes a deep learning framework for classification of BBC
television programmes using audio. The audio is firstly transformed into
spectrograms, which are fed into a pre-trained convolutional Neural Network
(CNN), obtaining predicted probabilities of sound events occurring in the audio
recording. Statistics for the predicted probabilities and detected sound events
are then calculated to extract discriminative features representing the
television programmes. Finally, the embedded features extracted are fed into a
classifier for classifying the programmes into different genres. Our
experiments are conducted over a dataset of 6,160 programmes belonging to nine
genres labelled by the BBC. We achieve an average classification accuracy of
93.7% over 14-fold cross validation. This demonstrates the efficacy of the
proposed framework for the task of audio-based classification of television
programmes.
