activity
20232026
most citedDifferentially Private Adapters for Parameter Efficient Acoustic Modeling

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

eess.AS2026

A Knowledge-Driven Approach to Target Speech Extraction in the Presence of Background Sound Effects for Cinematic Audio Source Separation (CASS)

Chun-wei Ho, Sabato Marco Siniscalchi, Kai Li +1

We propose a knowledge-driven approach to speech target extraction in the presence of background sound effects already recorded in cinematic audio. The specific knowledge sources s…

eess.AS2026

A Knowledge-Driven Approach to Music Segmentation, Music Source Separation and Cinematic Audio Source Separation

Chun-wei Ho, Sabato Marco Siniscalchi, Kai Li +1

We propose a knowledge-driven, model-based approach to segmenting audio into single-category and mixed-category chunks with applications to source separation. "Knowledge" here deno…

eess.AS2025

An Investigation on Combining Geometry and Consistency Constraints into Phase Estimation for Speech Enhancement

Chun-Wei Ho, Pin-Jui Ku, Hao Yen +3

We propose a novel iterative phase estimation framework, termed multi-source Griffin-Lim algorithm (MSGLA), for speech enhancement (SE) under additive noise conditions. The core id…

eess.AS2024

An Explicit Consistency-Preserving Loss Function for Phase Reconstruction and Speech Enhancement

Pin-Jui Ku, Chun-Wei Ho, Hao Yen +2

In this work, we propose a novel consistency-preserving loss function for recovering the phase information in the context of phase reconstruction (PR) and speech enhancement (SE).…

cs.SD2023★ 1 cited

Differentially Private Adapters for Parameter Efficient Acoustic Modeling

Chun-Wei Ho, Chao-Han Huck Yang, Sabato Marco Siniscalchi

In this work, we devise a parameter-efficient solution to bring differential privacy (DP) guarantees into adaptation of a cross-lingual speech classifier. We investigate a new froz…