Music-visual feature mapping and deep tagging

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Apollo Music Store is a Montreal based company that specializes on licensing and distributing digitally music.They represented hundreds of artists and more than 75% of them are Canadian. The goal of this project is toimprove the user experience of Apollo customers who are trying to find specific kinds of music for advertising,games and other applications. Two main objectives will be pursued. The first is to explore how toautomatically associate and adjust stock video with music tracks based on audio and visual feature extractionand mapping. The second objective is to use deep learning techniques to build software that automatically tagsthe music with descriptive keywords. Deep learning has been shown to be very effective in a variety ofmachine learning tasks. Data augmentation and semi-supervised learning are techniques used to train deepneural networks when the amount of labeled data is limited. Apollo Music will provide access to their digitalcatalog as well as existing meta-data their curators have provided about the tracks. The research group ofDr.Tzanetakis has a strong international presence in Music Information Retrieval and Machine Learning.which are research areas that will contribute ideas, concepts, and algorithms to the project. Two PhD studentswith complementary experience in computer vision/deep learning/audio signal processing and gesturerecognition/human-computer interaction will be funded through the project and work with the designers andengineers at Apollo Music Store.