63 citations · 87 across the 13 of their papers we have counts for
16 papers
Unsupervised Instantaneous Phase and Frequency Tracking by Inverse Voice Synthesis
Chin-Yun Yu, György Fazekas
Knowledge-driven neural vocoders struggle to learn reliable fundamental frequency end-to-end, because spectral objectives provide weak supervision of periodic structure and lack ph…
Pitch Smoothing Using Relative Interval Networks
Chin-Yun Yu, Chi-Jen Peng, Li Su +1
Pitch tracking systems typically couple a per-frame fundamental frequency () estimator with a temporal smoothing stage to obtain continuous trajectories. Conventional Viterbi…
Accelerating Automatic Differentiation of Direct Form Digital Filters
Chin-Yun Yu, György Fazekas
We introduce a general formulation for automatic differentiation through direct form filters, yielding a closed-form backpropagation that includes initial condition gradients. The…
Sound Matching an Analogue Levelling Amplifier Using the Newton-Raphson Method
Chin-Yun Yu, György Fazekas
Automatic differentiation through digital signal processing algorithms for virtual analogue modelling has recently gained popularity. These algorithms are typically more computatio…
Improving Inference-Time Optimisation for Vocal Effects Style Transfer with a Gaussian Prior
Chin-Yun Yu, Marco A. Martínez-Ramírez, Junghyun Koo +3
Style Transfer with Inference-Time Optimisation (ST-ITO) is a recent approach for transferring the applied effects of a reference audio to an audio track. It optimises the effect p…
DiffVox: A Differentiable Model for Capturing and Analysing Vocal Effects Distributions
Chin-Yun Yu, Marco A. Martínez-Ramírez, Junghyun Koo +4
This study introduces a novel and interpretable model, DiffVox, for matching vocal effects in music production. DiffVox, short for ``Differentiable Vocal Fx", integrates parametric…
Differentiable Time-Varying Linear Prediction in the Context of End-to-End Analysis-by-Synthesis
Chin-Yun Yu, György Fazekas
Training the linear prediction (LP) operator end-to-end for audio synthesis in modern deep learning frameworks is slow due to its recursive formulation. In addition, frame-wise app…
Time-of-arrival Estimation and Phase Unwrapping of Head-related Transfer Functions With Integer Linear Programming
Chin-Yun Yu, Johan Pauwels, György Fazekas
In binaural audio synthesis, aligning head-related impulse responses (HRIRs) in time has been an important pre-processing step, enabling accurate spatial interpolation and efficien…
Differentiable All-pole Filters for Time-varying Audio Systems
Chin-Yun Yu, Christopher Mitcheltree, Alistair Carson +3
Infinite impulse response filters are an essential building block of many time-varying audio systems, such as audio effects and synthesisers. However, their recursive structure imp…
Zero-Shot Duet Singing Voices Separation with Diffusion Models
Chin-Yun Yu, Emilian Postolache, Emanuele Rodolà +1
In recent studies, diffusion models have shown promise as priors for solving audio inverse problems. These models allow us to sample from the posterior distribution of a target sig…
TorchAudio 2.1: Advancing speech recognition, self-supervised learning, and audio processing components for PyTorch
Jeff Hwang, Moto Hira, Caroline Chen +21
TorchAudio is an open-source audio and speech processing library built for PyTorch. It aims to accelerate the research and development of audio and speech technologies by providing…
Singing Voice Synthesis Using Differentiable LPC and Glottal-Flow-Inspired Wavetables
Chin-Yun Yu, György Fazekas
This paper introduces GlOttal-flow LPC Filter (GOLF), a novel method for singing voice synthesis (SVS) that exploits the physical characteristics of the human voice using different…
Conditioning and Sampling in Variational Diffusion Models for Speech Super-Resolution
Chin-Yun Yu, Sung-Lin Yeh, György Fazekas +1
Recently, diffusion models (DMs) have been increasingly used in audio processing tasks, including speech super-resolution (SR), which aims to restore high-frequency content given l…
Danna-Sep: Unite to separate them all
Chin-Yun Yu, Kin-Wai Cheuk
Deep learning-based music source separation has gained a lot of interest in the last decades. Most of the existing methods operate with either spectrograms or waveforms. Spectrogra…
Music Demixing Challenge 2021
Yuki Mitsufuji, Giorgio Fabbro, Stefan Uhlich +6
Music source separation has been intensively studied in the last decade and tremendous progress with the advent of deep learning could be observed. Evaluation campaigns such as MIR…
Multi-layered Cepstrum for Instantaneous Frequency Estimation
Chin-Yun Yu, Li Su
We propose the multi-layered cepstrum (MLC) method to estimate multiple fundamental frequencies (MF0) of a signal under challenging contamination such as high-pass filter noise. Ta…