Robust soccer highlight generation with a novel dominant-speech feature extractor
We describe soccer highlight generation from only the audio stream in the video. A novel audio feature is used to detect parts of the commentary corresponding to dominant and excited speech. It is computed by a twice-iterated composite Fourier transform (CFT) on short-time windows, wherein the magnitude spectrum of the first transform is input to a second transform. Dominant speech portions are found to be robustly characterized by increased density in the peak profile. We verify the robustness of CFT via large scale empirical testing and explain its working based on a pulse train postulate of dominant speech signals. Our audio-only approach results in a compute-efficient system deployable on current generation set-top-boxes and digital video recording devices