Multimodal Kernel Method for Activity Detection of Sound Sources

We consider the problem of acoustic scene analysis of multiple sound sources. In our setting, the sound sources are measured by a single microphone, and a particular source of interest is also captured by a video camera during a short time interval. The goal in this paper is to detect the activity of the source of interest even when the video data are missing, while ignoring the other sound sources. To address this problem, we propose a kernel-based algorithm that incorporates the audio-visual data by a combination of affinity kernels, constructed separately from the audio and the video data. We introduce a distance measure between data points that is associated with the source of interest, while reducing the effect of the other (interfering) sources. Using this distance, we devise a measure for the presence of the source of interest, which is naturally extended to time intervals, in which only the audio signal is available. Experimental results demonstrate the improved performance of the proposed algorithm compared to competing approaches implying the significance of the video signal in the analysis of complex acoustic scenes.