3 citations · 3 across the 9 of their papers we have counts for
12 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…
A Review of Differentiable Digital Signal Processing for Music & Speech Synthesis
Ben Hayes, Jordie Shier, György Fazekas +2
The term "differentiable digital signal processing" describes a family of techniques in which loss function gradients are backpropagated through digital signal processors, facilita…
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…
Mesostructures: Beyond Spectrogram Loss in Differentiable Time-Frequency Analysis
Cyrus Vahidi, Han Han, Changhong Wang +3
Computer musicians refer to mesostructures as the intermediate levels of articulation between the microstructure of waveshapes and the macrostructure of musical forms. Examples of…