Audio-Visual Fusion

The perception that we have about the world is influenced by elements of diverse nature. Indeed humans tend to integrate information coming from different sensory modalities to better understand their environment. Following this observation, scientists have been trying to combine different research domains. In particular, in joint audio-visual signal processing the information recorded with one or more video-cameras and one or more microphones is combined in order to extract more knowledge about a given scene than when analyzing each modality separately. In this thesis we attempt the fusion of audio and video modalities when considering one video-camera and one microphone. This is the most common configuration in electronic devices such as laptops and cellphones, and it does not require controlled environments such as previously prepared meeting rooms. Even though numerous approaches have been proposed in the last decade, the fusion of audio and video modalities is still an open problem. All the methods in this domain are based on an assumption of synchrony between related events in audio and video channels, i.e. the appearance of a sound is approximately synchronous with the movement of the image structure that has generated it. However, most approaches do not exploit the spatio-temporal consistency that characterizes video signals and, as a result, they assess the synchrony between single pixels and the soundtrack. The results that they obtain are thus sensitive to noise and the coherence between neighboring pixels is not ensured. This thesis presents two novel audio-visual fusion methods which follow completely different strategies to evaluate the synchrony between moving image structures and sounds. Each fusion method is successfully demonstrated on a different application in this domain. Our first audio-visual fusion approach is focused on the modeling of audio and video signals. We propose to decompose each modality into a small set of functions representing the structures that are inherent in the signals. The audio signal is decomposed into a set of atoms representing concentrations of energy in the spectrogram (sounds) and the video signal is concisely represented by a set of image structures evolving through time, i.e. changing their location, size or orientation. As a result, meaningful features can be easily defined for each modality, as the presence of a sound and the movement of a salient image structure. Finally, the fusion step simply evaluates the co-occurrence of these relevant events. This approach is applied to the blind detection and separation of the audio-visual sources that are present in a scene. In contrast, the second method that we propose uses basic features and it is more focused on the fusion strategy that combines them. This approach is based on a nonlinear diffusion procedure that progressively erodes a video sequence and converts it into an audio-visual video sequence, where only the information that is required in applications in the joint audio-visual domain is kept. For this purpose we define a diffusion coefficient that depends on the synchrony between video motion and audio energy and preserves regions moving coherently with the presence of sounds. Thus, the regions that are least diffused are likely to be part of the video modality of the audio-visual source, and the application of this fusion method to the unsupervised extraction of audio-visual objects is straightforward. Unlike many methods in this domain which are specific to speakers, the fusion methods that we present in this thesis are completely general and they can be applied to all kind of audio-visual sources. Furthermore, our analysis is not limited to one source at a time, i.e. all applications can deal with multiple simultaneous sources. Finally, this thesis tackles the audio-visual fusion problem from a novel perspective, by proposing creative fusion methods and techniques borrowed from other domains such as the blind source separation, nonlinear diffusion based on partial differential equations (PDE) and graph cut segmentation.