3 papers
eess.AS2026
Unseen but not Unknown: Using Dataset Concealment to Robustly Evaluate Speech Quality Estimation Models
Jaden Pieper, Stephen D. Voran
We introduce Dataset Concealment (DSC), a rigorous new procedure for evaluating and interpreting objective speech quality estimation models. DSC quantifies and decomposes the perfo…
eess.AS2024
AlignNet: Learning dataset score alignment functions to enable better training of speech quality estimators
Jaden Pieper, Stephen D. Voran
We develop two complementary advances for training no-reference (NR) speech quality estimators with independent datasets. Multi-dataset finetuning (MDF) pretrains an NR estimator o…
eess.AS2024
Why some audio signal short-time Fourier transform coefficients have nonuniform phase distributions
Stephen D. Voran
The short-time Fourier transform (STFT) represents a window of audio samples as a set of complex coefficients. These are advantageously viewed as magnitudes and phases and the over…