Zobrazeno 1 - 10
of 925
pro vyhledávání: '"Zhang, YiXiao"'
Autor:
Zang, Yongyi, Zhang, Yixiao
Large-scale text-to-music generation models have significantly enhanced music creation capabilities, offering unprecedented creative freedom. However, their ability to collaborate effectively with human musicians remains limited. In this paper, we pr
Externí odkaz:
http://arxiv.org/abs/2407.10328
We investigate the impact of a high-degree vertex in Tur\'{a}n problems for degenerate hypergraphs (including graphs). We say an $r$-graph $F$ is bounded if there exist constants $\alpha, \beta>0$ such that for large $n$, every $n$-vertex $F$-free $r
Externí odkaz:
http://arxiv.org/abs/2407.00427
Autor:
Zhang, Yixiao, Ikemiya, Yukara, Choi, Woosung, Murata, Naoki, Martínez-Ramírez, Marco A., Lin, Liwei, Xia, Gus, Liao, Wei-Hsiang, Mitsufuji, Yuki, Dixon, Simon
Recent advances in text-to-music editing, which employ text queries to modify music (e.g.\ by changing its style or adjusting instrumental components), present unique challenges and opportunities for AI-assisted music creation. Previous approaches in
Externí odkaz:
http://arxiv.org/abs/2405.18386
Autor:
Liu, Jie, Zhang, Yixiao, Wang, Kang, Yavuz, Mehmet Can, Chen, Xiaoxi, Yuan, Yixuan, Li, Haoliang, Yang, Yang, Yuille, Alan, Tang, Yucheng, Zhou, Zongwei
The advancement of artificial intelligence (AI) for organ segmentation and tumor detection is propelled by the growing availability of computed tomography (CT) datasets with detailed, per-voxel annotations. However, these AI models often struggle wit
Externí odkaz:
http://arxiv.org/abs/2405.18356
Autor:
Xiang, Tiange, Zhang, Yixiao, Lu, Yongyi, Yuille, Alan, Zhang, Chaoyi, Cai, Weidong, Zhou, Zongwei
Radiography imaging protocols focus on particular body regions, therefore producing images of great similarity and yielding recurrent anatomical structures across patients. Exploiting this structured information could potentially ease the detection o
Externí odkaz:
http://arxiv.org/abs/2403.08689
Controllable music generation plays a vital role in human-AI music co-creation. While Large Language Models (LLMs) have shown promise in generating high-quality music, their focus on autoregressive generation limits their utility in music editing tas
Externí odkaz:
http://arxiv.org/abs/2402.09508
Autor:
Zhang, Yixiao, Ikemiya, Yukara, Xia, Gus, Murata, Naoki, Martínez-Ramírez, Marco A., Liao, Wei-Hsiang, Mitsufuji, Yuki, Dixon, Simon
Recent advances in text-to-music generation models have opened new avenues in musical creativity. However, music generation usually involves iterative refinements, and how to edit the generated music remains a significant challenge. This paper introd
Externí odkaz:
http://arxiv.org/abs/2402.06178
Autor:
Manco, Ilaria, Weck, Benno, Doh, SeungHeon, Won, Minz, Zhang, Yixiao, Bogdanov, Dmitry, Wu, Yusong, Chen, Ke, Tovstogan, Philip, Benetos, Emmanouil, Quinton, Elio, Fazekas, György, Nam, Juhan
We introduce the Song Describer dataset (SDD), a new crowdsourced corpus of high-quality audio-caption pairs, designed for the evaluation of music-and-language models. The dataset consists of 1.1k human-written natural language descriptions of 706 mu
Externí odkaz:
http://arxiv.org/abs/2311.10057
Recent years have witnessed a rapid growth of large-scale language models in the domain of music audio. Such models enable end-to-end generation of higher-quality music, and some allow conditioned generation using text descriptions. However, the cont
Externí odkaz:
http://arxiv.org/abs/2310.17162
Creating music is iterative, requiring varied methods at each stage. However, existing AI music systems fall short in orchestrating multiple subsystems for diverse needs. To address this gap, we introduce Loop Copilot, a novel system that enables use
Externí odkaz:
http://arxiv.org/abs/2310.12404