1 citations · 2 across the 6 of their papers we have counts for
6 papers
Understanding Automatic Mixing: A Subtask-Oriented Analysis of Two-Stage Mixing System
Jinjie Shi, Wei Hua, Kunzhu Xie +3
Automatic mixing transforms multitrack recordings into perceptually coherent, balanced, and aesthetically consistent mixes. In real-world production, this task is challenging due t…
SPHERE: Automatic Music Upmixing via Audio Language Model Post-Training with Spatial Heuristic Rewards
Zixun Guo, Calvin Murdock, Sanjeel Parekh +4
In this paper, we study the task of automatic music upmixing, wherein a system predicts spatial mixing parameters from a multi-stem recording. Different from existing methods that…
Diff2Mix: Controllable Music Mixing via Diffusion Models and Differentiable Audio Effects
Yisu Zong, Jinjie Shi, Joshua Reiss
Automatic music mixing aims to combine multitrack recordings into a balanced and coherent musical piece. Because the content of different songs and the subjective preferences of mi…
EMORSION: Examining the Impact of Audio Parameters on Emotional Responses and Immersion in Film
Nelly Garcia, Ruby Crocker, Bleiz M Del Sette +4
EMORSION is an exploratory proof-of-concept study examining how film audio design shapes audience emotion and immersion in acinema setting. Four film scenes were selected across th…
ST-ITO: Controlling Audio Effects for Style Transfer with Inference-Time Optimization
Christian J. Steinmetz, Shubhr Singh, Marco Comunità +4
Audio production style transfer is the task of processing an input to impart stylistic elements from a reference recording. Existing approaches often train a neural network to esti…
Diff-MST: Differentiable Mixing Style Transfer
Soumya Sai Vanka, Christian Steinmetz, Jean-Baptiste Rolland +2
Mixing style transfer automates the generation of a multitrack mix for a given set of tracks by inferring production attributes from a reference song. However, existing systems for…