16 citations · 63 across the 38 of their papers we have counts for
5 papers · 1 filter
Unsupervised vocal dereverberation with diffusion-based generative models
Koichi Saito, Naoki Murata, Toshimitsu Uesaka +4
Removing reverb from reverberant music is a necessary technique to clean up audio for downstream music manipulations. Reverberation of music contains two categories, natural reverb…
DiffRoll: Diffusion-based Generative Music Transcription with Unsupervised Pretraining Capability
Kin Wai Cheuk, Ryosuke Sawata, Toshimitsu Uesaka +5
In this paper we propose a novel generative approach, DiffRoll, to tackle automatic music transcription (AMT). Instead of treating AMT as a discriminative task in which the model i…
Diffiner: A Versatile Diffusion-based Generative Refiner for Speech Enhancement
Ryosuke Sawata, Naoki Murata, Yuhta Takida +4
Although deep neural network (DNN)-based speech enhancement (SE) methods outperform the previous non-DNN-based ones, they often degrade the perceptual quality of generated outputs.…
FP-Diffusion: Improving Score-based Diffusion Models by Enforcing the Underlying Score Fokker-Planck Equation
Chieh-Hsin Lai, Yuhta Takida, Naoki Murata +3
Score-based generative models (SGMs) learn a family of noise-conditional score functions corresponding to the data density perturbed with increasingly large amounts of noise. These…
SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization
Yuhta Takida, Takashi Shibuya, WeiHsiang Liao +7
One noted issue of vector-quantized variational autoencoder (VQ-VAE) is that the learned discrete representation uses only a fraction of the full capacity of the codebook, also kno…