Voicing classification of visual speech using convolutional neural networks

The application of neural network and convolutional neural net- work (CNN) architectures is explored for the tasks of voicing classification (classifying frames as being either non-speech, unvoiced, or voiced) and voice activity detection (VAD) of vi- sual speech. Experiments are conducted for both speaker de- pendent and speaker independent scenarios. A Gaussian mixture model (GMM) baseline system is de- veloped using standard image-based two-dimensional discrete cosine transform (2D-DCT) visual speech features, achieving speaker dependent accuracies of 79% and 94%, for voicing classification and VAD respectively. Additionally, a single- layer neural network system trained using the same visual fea- tures achieves accuracies of 86 % and 97 %. A novel technique using convolutional neural networks for visual speech feature extraction and classification is presented. The voicing classifi- cation and VAD results using the system are further improved to 88 % and 98 % respectively. The speaker independent results show the neural network system to outperform both the GMM and CNN systems, achiev- ing accuracies of 63 % for voicing classification, and 79 % for voice activity detection

Weakly Supervised Audio Source Separation via Spectrum Energy Preserved Wasserstein Learning

Separating audio mixtures into individual instrument tracks has been a long standing challenging task. We introduce a novel weakly supervised audio source separation approach based on deep adversarial learning. Specifically, our loss function adopts the Wasserstein distance which directly measures the distribution distance between the separated sources and the real sources for each individual source. Moreover, a global regularization term is added to fulfill the spectrum energy preservation property regardless separation. Unlike state-of-the-art weakly supervised models which often involve deliberately devised constraints or careful model selection, our approach need little prior model specification on the data, and can be straightforwardly learned in an end-to-end fashion. We show that the proposed method performs competitively on public benchmark against state-of-the-art weakly supervised methods