We present the results where given an unconstrained video we recommend music from a large catalog based on the deep emotion representations learned from the two modalities.
We train a model to take the input of MIDI data, and output the visual performance as expressive body movements for pianist. It can be used for demonstration purpose for music learners, or immersive music enjoyment system, or human-computer interactions in automatic accompaniment systems. We show all the demo videos of the generated visual performance (as skeleton key points) compared with real human on same pieces.
We propose to leverage visual information captured from music performance videos to advance several music information retrieval (MIR) tasks, such as source association, multi-pitch analysis, and vibrato analysis.
We create an audio-visual, multi-track, and multi-instrument music performance dataset that comprises a number of chamber music assembled from coordinated but separately recorded performances of individual tracks. With ground-truth pitch/note annotations and clean individual audio tracks available, this can be used for multi-modal analysis of music performance.
We address the "sustained effect" in piano music performance, caused by the usage of sustained pedal or legato articulations. Due to this effect, the mixture of energy between the sustained and following notes (non-notated in the score) always results in delay erros in score following systems. We propose to modify the audio feature representations to reduce the sustained effect and enhance the robustness of score following systems.
Live musical performances (e.g., choruses, concerts, and operas) often require the display of lyrics for the convenience of the audience. We propose a computational system to automate this real-time lyrics display process using signal processing techniques