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Lucas Rencker

4 accepted papers

2021

Context-Aware Prosody Correction for Text-Based Speech Editing

ICASSP 2021accepted

Text-based speech editors expedite the process of editing speech recordings by permitting editing via intuitive cut, copy, and paste operations on a speech transcript. A major drawback of current systems, however, is that edited recordings often sound unnatural because of prosody mismatches around e…

Cited by 0SourceScholar
2018

Orthogonality-Regularized Masked NMF for Learning on Weakly Labeled Audio Data

ICASSP 2018accepted

Non-negative Matrix Factorization (NMF) is a well established tool for audio analysis. However, it is not well suited for learning on weakly labeled data, i.e. data where the exact timestamp of the sound of interest is not known. In this paper we propose a novel extension to NMF, that allows it to e…

Cited by 0SourceScholar
2017

A greedy algorithm with learned statistics for sparse signal reconstruction

ICASSP 2017accepted

We address the problem of sparse signal reconstruction from a few noisy samples. Recently, a Covariance-Assisted Matching Pursuit (CAMP) algorithm has been proposed, improving the sparse coefficient update step of the classic Orthogonal Matching Pursuit (OMP) algorithm. CAMP allows the a-priori mean…

Cited by 0SourceScholar
2017

Assessment of musical noise using localization of isolated peaks in time-frequency domain

ICASSP 2017accepted

Musical noise is a recurrent issue that appears in spectral techniques for denoising or blind source separation. Due to localised errors of estimation, isolated peaks may appear in the processed spectrograms, resulting in annoying tonal sounds after synthesis known as “musical noise”. In this paper,…

Cited by 0SourceScholar