activity
20202026
most citedPsychoacoustic Calibration of Loss Functions for Efficient End-to-End Neural Audio Coding

30 citations · 60 across the 5 of their papers we have counts for

collaborators

6 papers

math.OC2026

Explicit Bounds on Hoffman Constants for Sparse Polyhedral Regularizers

Faris Chaudhry, Anthea Monod, Keisuke Yano

Hoffman constants quantify the distance to the solution set of a polyhedral system in terms of its residual; they control error bounds, geometric conditioning, the convergence rate…

cs.SD2024★ 4 cited

Personalized Neural Speech Codec

Inseon Jang, Haici Yang, Wootaek Lim +2

In this paper, we propose a personalized neural speech codec, envisioning that personalization can reduce the model complexity or improve perceptual speech quality. Despite the com…

eess.AS2023

Hybrid noise shaping for audio coding using perfectly overlapped window

Byeongho Jo, Seungkwon Beack

In recent years, audio coding technology has been standardized based on several frameworks that incorporate linear predictive coding (LPC). However, coding the transient signal usi…

eess.AS2023

Audio coding with unified noise shaping and phase contrast control

Byeongho Jo, Seungkwon Beack, Taejin Lee

Over the past decade, audio coding technology has seen standardization and the development of many frameworks incorporated with linear predictive coding (LPC). As LPC reduces infor…

eess.AS2021★ 26 cited

Scalable and Efficient Neural Speech Coding: A Hybrid Design

Kai Zhen, Jongmo Sung, Mi Suk Lee +2

We present a scalable and efficient neural waveform coding system for speech compression. We formulate the speech coding problem as an autoencoding task, where a convolutional neur…

cs.SD2020★ 30 cited

Psychoacoustic Calibration of Loss Functions for Efficient End-to-End Neural Audio Coding

Kai Zhen, Mi Suk Lee, Jongmo Sung +2

Conventional audio coding technologies commonly leverage human perception of sound, or psychoacoustics, to reduce the bitrate while preserving the perceptual quality of the decoded…