Zobrazeno 1 - 10
of 501
pro vyhledávání: '"RAMÍREZ, MARCO"'
Autor:
Liao, WeiHsiang, Takida, Yuhta, Ikemiya, Yukara, Zhong, Zhi, Lai, Chieh-Hsin, Fabbro, Giorgio, Shimada, Kazuki, Toyama, Keisuke, Cheuk, Kinwai, Martínez-Ramírez, Marco A., Takahashi, Shusuke, Uhlich, Stefan, Akama, Taketo, Choi, Woosung, Koyama, Yuichiro, Mitsufuji, Yuki
We demonstrate the efficacy of using intermediate representations from a single foundation model to enhance various music downstream tasks. We introduce SoniDo, a music foundation model (MFM) designed to extract hierarchical features from target musi
Externí odkaz:
http://arxiv.org/abs/2411.01135
Autor:
Chae, Yunkee, Choi, Woosung, Takida, Yuhta, Koo, Junghyun, Ikemiya, Yukara, Zhong, Zhi, Cheuk, Kin Wai, Martínez-Ramírez, Marco A., Lee, Kyogu, Liao, Wei-Hsiang, Mitsufuji, Yuki
Recent state-of-the-art neural audio compression models have progressively adopted residual vector quantization (RVQ). Despite this success, these models employ a fixed number of codebooks per frame, which can be suboptimal in terms of rate-distortio
Externí odkaz:
http://arxiv.org/abs/2410.06016
Autor:
Mancusi, Michele, Halychanskyi, Yurii, Cheuk, Kin Wai, Lai, Chieh-Hsin, Uhlich, Stefan, Koo, Junghyun, Martínez-Ramírez, Marco A., Liao, Wei-Hsiang, Fabbro, Giorgio, Mitsufuji, Yuki
Music timbre transfer is a challenging task that involves modifying the timbral characteristics of an audio signal while preserving its melodic structure. In this paper, we propose a novel method based on dual diffusion bridges, trained using the Coc
Externí odkaz:
http://arxiv.org/abs/2409.06096
Autor:
Lee, Sungho, Martínez-Ramírez, Marco, Liao, Wei-Hsiang, Uhlich, Stefan, Fabbro, Giorgio, Lee, Kyogu, Mitsufuji, Yuki
We present GRAFX, an open-source library designed for handling audio processing graphs in PyTorch. Along with various library functionalities, we describe technical details on the efficient parallel computation of input graphs, signals, and processor
Externí odkaz:
http://arxiv.org/abs/2408.03204
Autor:
Chen, Yu-Hua, Choi, Woosung, Liao, Wei-Hsiang, Martínez-Ramírez, Marco, Cheuk, Kin Wai, Mitsufuji, Yuki, Jang, Jyh-Shing Roger, Yang, Yi-Hsuan
Recent years have seen increasing interest in applying deep learning methods to the modeling of guitar amplifiers or effect pedals. Existing methods are mainly based on the supervised approach, requiring temporally-aligned data pairs of unprocessed a
Externí odkaz:
http://arxiv.org/abs/2406.15751
Autor:
Lee, Sungho, Martínez-Ramírez, Marco A., Liao, Wei-Hsiang, Uhlich, Stefan, Fabbro, Giorgio, Lee, Kyogu, Mitsufuji, Yuki
Music mixing is compositional -- experts combine multiple audio processors to achieve a cohesive mix from dry source tracks. We propose a method to reverse engineer this process from the input and output audio. First, we create a mixing console that
Externí odkaz:
http://arxiv.org/abs/2406.01049
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:
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