Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  investigate techniques to localize the sound source in video made using one microphone. The visual object whose motion generates the sound is located and segmented based on the synchronization analysis of object motion and audio energy.  first apply an effective region tracking algorithm to segment the video into a number of spatial-temporal region tracks, each representing the temporal evolution of an appearance-coherent image structure (i.e., object).  then extract the motion features of each object as its average acceleration in each frame. Meanwhile, Short-term Fourier Transform is applied to the audio signal to extract audio energy feature as the audio descriptor.  further impose a nonlinear transformation on both audio and visual descriptors to obtain the audio and visual codes in a common rank correlation space. Finally, the correlation beten an object and the audio signal is simply evaluated by computing the Hamming distance beten the audio and visual codes generated in previous steps.  evaluate the proposed method both qualitatively and quantitatively using a number of challenging test videos. In particular, the proposed method is compared with a state-of-the-art audiovisual source localization algorithm. The results demonstrate the superior performance of the proposed algorithm in spatial-temporal localization and segmentation of audio sources in the visual domain.correlation space. Finally, the correlation beten an object and the audio signal is simply evaluated by computing the Hamming distance beten the audio and visual codes generated in previous steps.  evaluate the proposed method both qualitatively and quantitatively using a number of challenging test videos. In particular, the proposed method is compared with a state-of-the-art audiovisual source localization algorithm. The results demonstrate the superior performance of the proposed algorithm in spatial-temporal localization and segmentation of audio sources in the visual domain.
['Kai Li', 'Jun Ye', 'Kien A. Hua']
I am writing a research paper on a idea What's Making that Sound?
[{"from": "human", "value": "I am writing a research paper on a idea What's Making that Sound?"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  investigate techniques to localize the sound source in video made using one microphone. The visual object whose motion generates the sound is located and segmented based on the synchronization analysis of object motion and audio energy.  first apply an effective region tracking algorithm to segment the video into a number of spatial-temporal region tracks, each representing the temporal evolution of an appearance-coherent image structure (i.e., object).  then extract the motion features of each object as its average acceleration in each frame. Meanwhile, Short-term Fourier Transform is applied to the audio signal to extract audio energy feature as the audio descriptor.  further impose a nonlinear transformation on both audio and visual descriptors to obtain the audio and visual codes in a common rank correlation space. Finally, the correlation beten an object and the audio signal is simply evaluated by computing the Hamming distance beten the audio and visual codes generated in previous steps.  evaluate the proposed method both qualitatively and quantitatively using a number of challenging test videos. In particular, the proposed method is compared with a state-of-the-art audiovisual source localization algorithm. The results demonstrate the superior performance of the proposed algorithm in spatial-temporal localization and segmentation of audio sources in the visual domain.correlation space. Finally, the correlation beten an object and the audio signal is simply evaluated by computing the Hamming distance beten the audio and visual codes generated in previous steps.  evaluate the proposed method both qualitatively and quantitatively using a number of challenging test videos. In particular, the proposed method is compared with a state-of-the-art audiovisual source localization algorithm. The results demonstrate the superior performance of the proposed algorithm in spatial-temporal localization and segmentation of audio sources in the visual domain."}]