Short online videos have become the dominating media on social platforms. However, finding suitable music to accompany videos can be a challenging task to some video creators, due to copyright constraints, limitations in search engines, and required audio-editing expertise. One possible solution to these problems is to use AI music generation. In this paper we present a user interface (UI) paradigm that allows users to input a song to an AI engine and then interactively regenerate and mix AI-generated music. To arrive at this design, we conducted user studies with a total of 104 video creators at several stages of our design and development process. User studies supported the effectiveness of our approach and provided valuable insights about human-AI interaction as well as the design and evaluation of mixed-initiative interfaces in creative practice.

Voice recording is a challenging task with many pitfalls due to sub-par recording environments, mistakes in recording setup, microphone quality, etc. Newcomers to voice recording often have difficulty recording their voice, leading to recordings with low sound quality. Many amateur recordings of poor quality have two key problems: too much reverberation (echo), and too much background noise (e.g. fans, electronics, street noise). We present VoiceAssist, a system that helps inexperienced users produce high quality recordings by providing real-time visual feedback on audio quality. We integrate modern audio quality measures into an interactive human-machine feedback loop, so that the audio quality can be maximized at capture-time. We demonstrate the utility of this feedback for improving the recording quality with a user study. When presented with visual feedback about recording quality, users produced recordings that were strongly preferred by third-party listeners, when compared to recordings made without this feedback.