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Iterative Audio Separation with Mixture Consistency via MIMO Model Extension
Yukara Ikemiya, WeiHsiang Liao, Yuki Mitsufuji
This paper proposes a general framework for stable and effective iterative audio separation with mixture consistency by extending source separation models to a multi-input multi-ou…
Break-the-Beat! Controllable MIDI-to-Drum Audio Synthesis
Shuyang Cui, Zhi Zhong, Qiyu Wu +9
Current methods for creating drum loop audio in digital music production, such as using one-shot samples or resampling, often demand non-trivial efforts of creators. While recent g…
Large-Scale Training Data Attribution for Music Generative Models via Unlearning
Woosung Choi, Junghyun Koo, Kin Wai Cheuk +7
This paper explores the use of unlearning methods for training data attribution (TDA) in music generative models trained on large-scale datasets. TDA aims to identify which specifi…
CCStereo: Audio-Visual Contextual and Contrastive Learning for Binaural Audio Generation
Yuanhong Chen, Kazuki Shimada, Christian Simon +3
Binaural audio generation (BAG) aims to convert monaural audio to stereo audio using visual prompts, requiring a deep understanding of spatial and semantic information. However, cu…
Music Foundation Model as Generic Booster for Music Downstream Tasks
WeiHsiang Liao, Yuhta Takida, Yukara Ikemiya +13
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…
Variable Bitrate Residual Vector Quantization for Audio Coding
Yunkee Chae, Woosung Choi, Yuhta Takida +8
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 c…