ICASSP SP Cadenza挑战赛：面向助听器的音乐分离/重混
The ICASSP SP Cadenza Challenge: Music Demixing/Remixing for Hearing Aids
AI总结：
本研究介绍2024年ICASSP SP Cadenza助听器音乐分离/重混挑战赛的设计与结果，该赛要求分解并重混音乐，17个参赛系统中9个超基线，最优方案为模型集成。
摘要：
本文报告了2024年ICASSP SP Cadenza挑战赛——面向助听器的音乐分离/重混的赛题设计与结果。Cadenza项目致力于提升听损人群的音乐音频质量。本次挑战赛的场景为通过助听器聆听扬声器播放的立体声音乐，任务是：将流行/摇滚音乐分解为人声、鼓、贝斯及其他（VDBO）；按指定增益重新平衡不同音轨，再重混为立体声，同时也接受端到端方案。共有11支队伍提交了17个系统。因果系统的表现弱于非因果方法，有9个系统优于基线系统。常见方法是对预训练分离模型进行微调，表现最佳的方案采用了模型集成。
英文摘要：
This paper reports on the design and results of the 2024 ICASSP SP Cadenza Challenge: Music Demixing/Remixing for Hearing Aids. The Cadenza project is working to enhance the audio quality of music for those with a hearing loss. The scenario for the challenge was listening to stereo reproduction over loudspeakers via hearing aids. The task was to: decompose pop/rock music into vocal, drums, bass and other (VDBO); rebalance the different tracks with specified gains and then remixing back to stereo. End-to-end approaches were also accepted. 17 systems were submitted by 11 teams. Causal systems performed poorer than non-causal approaches. 9 systems beat the baseline. A common approach was to fine-tuning pretrained demixing models. The best approach used an ensemble of models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
