Audio-track classification using deep neural networks

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Bell Media Inc. is the mass media subsidiary of Bell Canada Enterprises. A significant problem faced by the company is the absence of an efficient audio-track identification scheme. The audio-tracks of Bell digital media could be stereo, surround sound packages, mono or speech (described video). Although there are standards for tagging audio channels with metadata, many media production facilities (including Bell) do not adhere to these standards. In fact, in many cases there is no metadata tags, or the tracks are not labeled properly. Currently the company uses a manual approach to classify unlabeled audio tracks, which is inefficient, and time consuming. The objective of this project therefore, is to develop a new deep neural network based audio track identification that Bell can use on its audio tracks effectively and efficiently. Labeling the audio tracks will allow Bell to increase the amount of automation in their workflow, thereby increasing efficiency and accuracy, while at the same time improving their overall product development.****