arXiv Machine Learning By Nghia Phan, Rong Jin, Gang Liu, Xiao Dong

Enhancing Automatic Chord Recognition via Pseudo-Labeling and Knowledge Distillation

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arXiv:2602. 19778v4 Announce Type: replace-cross Abstract: Automatic Chord Recognition (ACR) is constrained by the scarcity of aligned chord labels, as well-aligned annotations are costly to acquire.

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arXiv Computation and Language
Sep 25

Reward-Tilted On-Policy Distillation for Acoustic Grounding in Audio-Language Models

The paper introduces Reward‑Tilted On‑Policy Distillation (RT‑OPD), a method that enhances acoustic grounding in audio‑language models by using a frozen teacher to generate a reward based on the contrast between token predictions with and without audio. This reward reshapes the teacher distribution for reverse‑KL distillation, encouraging students to rely more on acoustic evidence. Experiments on two compact students across three benchmarks show that RT‑OPD consistently outperforms vanilla OPD, and a 3B model trained with RT‑OPD achieves 72.72% accuracy on the MMAU benchmark, surpassing other 3B models and rivaling larger 7B and 8B models.

By Kaiyang Li, Shaobo Han, Yue Tian, Shihao Ji
arXiv AI
Jun 12

CMI-RewardBench: Evaluating Music Reward Models with Compositional Multimodal Instruction

arXiv:2603. 00610v3 Announce Type: replace-cross Abstract: While music generation models have evolved to handle complex multimodal inputs mixing text, lyrics, and reference audio, evaluation mechanisms have lagged behind.

By Yinghao Ma, Haiwen Xia, Hewei Gao, Weixiong Chen, Yuxin Ye, Yuchen Yang, Sungkyun Chang, Mingshuo Ding, Yizhi Li, Ruibin Yuan, Simon Dixon, Emmanouil Benetos