Development of a deep learning system for hummed melody identification for BertsoBot

The system introduced in this work tries to solve the problem of melody classification. The proposed approach is based on extracting the spectrogram of the audio of each melody and then using deep supervised learning approaches to classify them into categories. As found out experimentally, the Transfer Learning technique is required alongside Data Augmentation in order to improve the accuracy of the system. The results shown in this thesis, focus further work on this field by providing insight on the performance of different tested Learning Models. Overall, DenseNets have proved themselves the best architectures o use in this context reaching a significant prediction accuracy

Capture, Learning, and Synthesis of 3D Speaking Styles

Audio-driven 3D facial animation has been widely explored, but achieving realistic, human-like performance is still unsolved. This is due to the lack of available 3D datasets, models, and standard evaluation metrics. To address this, we introduce a unique 4D face dataset with about 29 minutes of 4D scans captured at 60 fps and synchronized audio from 12 speakers. We then train a neural network on our dataset that factors identity from facial motion. The learned model, VOCA (Voice Operated Character Animation) takes any speech signal as input - even speech in languages other than English - and realistically animates a wide range of adult faces. Conditioning on subject labels during training allows the model to learn a variety of realistic speaking styles. VOCA also provides animator controls to alter speaking style, identity-dependent facial shape, and pose (i.e. head, jaw, and eyeball rotations) during animation. To our knowledge, VOCA is the only realistic 3D facial animation model that is readily applicable to unseen subjects without retargeting. This makes VOCA suitable for tasks like in-game video, virtual reality avatars, or any scenario in which the speaker, speech, or language is not known in advance. We make the dataset and model available for research purposes at http://voca.is.tue.mpg.de.Comment: To appear in CVPR 201