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A novel multimodal source separation approach is proposed for physically moving and stationary sources which exploits a circular microphone array, multiple video cameras, robust spatial beamforming and time-frequency masking. The challenge of separating moving sources, including higher reverberation time (RT) even for physically stationary sources, is that the mixing filters are time varying; as such the unmixing filters should also be time varying but these are difficult to determine from only audio measurements. Therefore in the proposed approach, visual modality is used to facilitate the separation for both stationary and moving sources. The movement of the sources is detected by a three-dimensional tracker based on a Markov Chain Monte Carlo particle filter. The audio separation is performed by a robust least squares frequency invariant data-independent beamformer. The uncertainties in source localisation and direction of arrival information obtained from the 3D video-based tracker are controlled by using a convex optimisation approach in the beamformer design. In the final stage, the separated audio sources are further enhanced by applying a binary time-frequency masking technique in the cepstral domain. Experimental results show that using the visual modality, the proposed algorithm cannot only achieve performance better than conventional frequency-domain source separations algorithms, but also provide acceptable separation performance for moving sources.

Human emotions can be presented in data with multiple modalities, e.g. video, audio and text. An automated system for emotion recognition needs to consider a number of challenging issues, including feature extraction, and dealing with variations and noise in data. Deep learning have been extensively used recently, offering excellent performance in emotion recognition. This work presents a new method based on audio and visual modalities, where visual cues facilitate the detection of the speech or non-speech frames and the emotional state of the speaker. Different from previous works, we propose the use of novel speech features, e.g. the Wavegram, which is extracted with a one-dimensional Convolutional Neural Network (CNN) learned directly from time-domain waveforms, and Wavegram-Logmel features which combines the Wavegram with the log mel spectrogram. The system is then trained in an end-to-end fashion on the SAVEE database by also taking advantage of the correlations among each of the streams. It is shown that the proposed approach outperforms the traditional and state-of-the art deep learning based approaches, built separately on auditory and visual handcrafted features for the prediction of spontaneous and natural emotions.

Abstract. Speech source separation has been of great interest for a long time, leading to two major approaches. One of them is based on statistical properties of the signals and mixing process known as blind source separation (BSS). The other approach named as computational auditory scene analysis (CASA) is inspired by human auditory system and exploits monaural and binaural cues. In this paper these two approaches are studied and compared in more depth.