36 citations · 42 across the 12 of their papers we have counts for
14 papers
FLOMSS: Generative Music Source Separation by Flow Matching
Shuyang Cui, Zhi Zhong, Keisuke Toyama +10
Music source separation (MSS) has been conventionally addressed by discriminative methods. Recently, flow matching has emerged as a strong generative paradigm for modeling complex…
Distilling Diffusion Score Discrepancy for Efficient Training Data Attribution
Shixuan Liu, Joan Serrà, Kin Wai Cheuk +6
Training data attribution for diffusion models aims to identify the training samples that influence a generated instance, but existing methods either require costly per-sample grad…
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…
SpecMaskGIT: Masked Generative Modeling of Audio Spectrograms for Efficient Audio Synthesis and Beyond
Marco Comunità, Zhi Zhong, Akira Takahashi +7
Recent advances in generative models that iteratively synthesize audio clips sparked great success to text-to-audio synthesis (TTA), but with the cost of slow synthesis speed and h…
Instruct-MusicGen: Unlocking Text-to-Music Editing for Music Language Models via Instruction Tuning
Yixiao Zhang, Yukara Ikemiya, Woosung Choi +7
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 an…
MusicMagus: Zero-Shot Text-to-Music Editing via Diffusion Models
Yixiao Zhang, Yukara Ikemiya, Gus Xia +5
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…
HQ-VAE: Hierarchical Discrete Representation Learning with Variational Bayes
Yuhta Takida, Yukara Ikemiya, Takashi Shibuya +8
Vector quantization (VQ) is a technique to deterministically learn features with discrete codebook representations. It is commonly performed with a variational autoencoding model,…
Automatic Piano Transcription with Hierarchical Frequency-Time Transformer
Keisuke Toyama, Taketo Akama, Yukara Ikemiya +3
Taking long-term spectral and temporal dependencies into account is essential for automatic piano transcription. This is especially helpful when determining the precise onset and o…
Singing Voice Separation and Vocal F0 Estimation based on Mutual Combination of Robust Principal Component Analysis and Subharmonic Summation
Yukara Ikemiya, Katsutoshi Itoyama, Kazuyoshi Yoshii
This paper presents a new method of singing voice analysis that performs mutually-dependent singing voice separation and vocal fundamental frequency (F0) estimation. Vocal F0 estim…