A recording engineer's job is to faithfully record every instrument and vocal track with as much clarity -- and as little signal processing -- as possible. In recording terminology, signal processing is any kind of compression, distortion or other effects that alter the sound of the recording. The mixing engineer takes each separate instrumental and vocal track -- perhaps dozens for a single song -- and tweaks their volume, stereo pan and other settings to achieve a balanced, satisfying whole. Even though this is called the final mix, nothing's final until it's passed through the hands of the mastering engineer. A mastering session is called finishing, because this is where each song on a CD receives the final adjustments that make it sound great on vinyl, CD, MP3 or radio. Each different playback medium requires its own special equalizing, balancing and compression to make the music clear and powerful for the listener.|In this paper, we present a dynamic convolution kernel (DCK) strategy for convolutional neural networks. Using a fully convolutional network with the proposed DCKs, high-quality talking-face video can be generated from multi-modal sources (i.e., unmatched audio and video) in real time, and our trained model is robust to different identities, head postures, and input audios. Our proposed DCKs are specially designed for audio-driven talking face video generation, leading to a simple yet effective end-to-end system. We also provide a theoretical analysis to interpret why DCKs work. Experimental results show that our method can generate high-quality talking-face video with background at 60606060 fps. Comparison and evaluation between our method and the state-of-the-art methods demonstrate the superiority of our method. TALKING-FACE video refers to video which mainly focuses on head or upper body of the speaker given audio or text signals. In this paper, we propose an audio-driven talking-face system, capable of transferring the input talking-face video to a generated one corresponding to the input audio.
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