From the 1 of 10 linked papers with an AI index.
10 papers
Towards Out-of-Distribution Detection in Vocoder Recognition via Latent Feature Reconstruction
Renmingyue Du, Jixun Yao, Qiuqiang Kong +1
The paper proposes a reconstruction‑based method using autoencoders to detect out‑of‑distribution vocoder samples by reconstructing WavLM acoustic features, with contrastive learni…
Voices of Civilizations: A Multilingual QA Benchmark for Global Music Understanding
Shangda Wu, Ziya Zhou, Yongyi Zang +4
We introduce Voices of Civilizations, the first multilingual QA benchmark for evaluating audio LLMs' cultural comprehension on full-length music recordings. Covering 380 tracks acr…
Summary of The Inaugural Music Source Restoration Challenge
Yongyi Zang, Jiarui Hai, Wanying Ge +5
Music Source Restoration (MSR) aims to recover original, unprocessed instrument stems from professionally mixed and degraded audio, requiring the reversal of both production effect…
MSRBench: A Benchmarking Dataset for Music Source Restoration
Yongyi Zang, Jiarui Hai, Wanying Ge +5
Music Source Restoration (MSR) extends source separation to realistic settings where signals undergo production effects (equalization, compression, reverb) and real-world degradati…
Music Source Restoration
Yongyi Zang, Zheqi Dai, Mark D. Plumbley +1
We introduce Music Source Restoration (MSR), a novel task addressing the gap between idealized source separation and real-world music production. Current Music Source Separation (M…
Training-Free Multi-Step Audio Source Separation
Yongyi Zang, Jingyi Li, Qiuqiang Kong
Audio source separation aims to separate a mixture into target sources. Previous audio source separation systems usually conduct one-step inference, which does not fully explore th…