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20102026
most citedDomain Adaptive Neural Networks for Object Recognition

94 citations · 404 across the 41 of their papers we have counts for

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10 papers · 1 filter

cs.SD2026

Unsupervised Speech Enhancement via Drifting

Diego Caviedes-Nozal, Liang Xu, Rasmus Kongsgaard Olsson +1

This paper addresses unsupervised speech enhancement in the unpaired setting using drifting methods, where training relies on separate collections of degraded and clean audio witho…

cs.SD2026

DriftSE: Speech Enhancement with Generative Drifting

Liang Xu, Diego Caviedes-Nozal, W. Bastiaan Kleijn +2

We propose DriftSE, a novel one-step generative framework for speech enhancement formulated as a latent distribution equilibrium problem. During training, the drifting field aligns…

cs.SD2026

Speech Enhancement Based on Drifting Models

Liang Xu, Diego Caviedes-Nozal, W. Bastiaan Kleijn +2

We propose Speech Enhancement based on Drifting Models (DriftSE), a novel generative framework that formulates denoising as an equilibrium problem. Rather than relying on iterative…

cs.SD2026

Single Microphone Own Voice Detection based on Simulated Transfer Functions for Hearing Aids

Mathuranathan Mayuravaani, W. Bastiaan Kleijn, Andrew Lensen +1

This paper presents a simulation-based approach to own voice detection (OVD) in hearing aids using a single microphone. While OVD can significantly improve user comfort and speech…

cs.SD2023★ 1 cited

LMCodec: A Low Bitrate Speech Codec With Causal Transformer Models

Teerapat Jenrungrot, Michael Chinen, W. Bastiaan Kleijn +4

We introduce LMCodec, a causal neural speech codec that provides high quality audio at very low bitrates. The backbone of the system is a causal convolutional codec that encodes au…

cs.SD2022

Ultra-Low-Bitrate Speech Coding with Pretrained Transformers

Ali Siahkoohi, Michael Chinen, Tom Denton +2

Speech coding facilitates the transmission of speech over low-bandwidth networks with minimal distortion. Neural-network based speech codecs have recently demonstrated significant…

cs.SD2022

On the Relevance of Bandwidth Extension for Speaker Verification

Marcos Faundez-Zanuy, Mattias Nilsson, W. Bastiaan Kleijn

In this paper, we consider the effect of a bandwidth extension of narrow-band speech signals (0.3-3.4 kHz) to 0.3-8 kHz on speaker verification. Using covariance matrix based verif…

cs.SD2022

On the relevance of bandwidth extension for speaker identification

Marcos Faundez-Zanuy, Mattias Nilsson, W. Bastiaan Kleijn

In this paper we discuss the relevance of bandwidth extension for speaker identification tasks. Mainly we want to study if it is possible to recognize voices that have been bandwit…

cs.SD2017★ 60 cited

An evaluation of intrusive instrumental intelligibility metrics

Steven Van Kuyk, W. Bastiaan Kleijn, Richard C. Hendriks

Instrumental intelligibility metrics are commonly used as an alternative to listening tests. This paper evaluates 12 monaural intrusive intelligibility metrics: SII, HEGP, CSII, HA…

cs.SD2017★ 46 cited

An instrumental intelligibility metric based on information theory

Steven Van Kuyk, W. Bastiaan Kleijn, Richard C. Hendriks

We propose a monaural intrusive instrumental intelligibility metric called speech intelligibility in bits (SIIB). SIIB is an estimate of the amount of information shared between a…