28 citations · 169 across the 76 of their papers we have counts for
32 papers · 1 filter
CMI-Bench: A Comprehensive Benchmark for Evaluating Music Instruction Following
Yinghao Ma, Siyou Li, Juntao Yu +2
Recent advances in audio-text large language models (LLMs) have opened new possibilities for music understanding and generation. However, existing benchmarks are limited in scope,…
Enhancing Lyrics Transcription on Music Mixtures with Consistency Loss
Jiawen Huang, Felipe Sousa, Emir Demirel +2
Automatic Lyrics Transcription (ALT) aims to recognize lyrics from singing voices, similar to Automatic Speech Recognition (ASR) for spoken language, but faces added complexity due…
From Aesthetics to Human Preferences: Comparative Perspectives of Evaluating Text-to-Music Systems
Huan Zhang, Jinhua Liang, Huy Phan +2
Evaluating generative models remains a fundamental challenge, particularly when the goal is to reflect human preferences. In this paper, we use music generation as a case study to…
YuE: Scaling Open Foundation Models for Long-Form Music Generation
Ruibin Yuan, Hanfeng Lin, Shuyue Guo +55
We tackle the task of long-form music generation--particularly the challenging \textbf{lyrics-to-song} problem--by introducing YuE, a family of open foundation models based on the…
YourMT3+: Multi-instrument Music Transcription with Enhanced Transformer Architectures and Cross-dataset Stem Augmentation
Sungkyun Chang, Emmanouil Benetos, Holger Kirchhoff +1
Multi-instrument music transcription aims to convert polyphonic music recordings into musical scores assigned to each instrument. This task is challenging for modeling as it requir…
Towards Building an End-to-End Multilingual Automatic Lyrics Transcription Model
Jiawen Huang, Emmanouil Benetos
Multilingual automatic lyrics transcription (ALT) is a challenging task due to the limited availability of labelled data and the challenges introduced by singing, compared to multi…