Online Speech Dereverberation Using RLS-WPE Based on a Full Spatial Correlation Matrix Integrated in a Speech Enhancement System

Kim, Jun Hyung; Park, Jazzson; Ahn, Minwook; Lee, Yeonbok; Kim, Wonsub; Park, Hyung‐Min

doi:10.1109/iwaenc.2018.8521285

Cited by 1 publication

(1 citation statement)

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“…Nonlinear adaptive techniques include, for example, artificial neural networks (ANN), methods using hybrid neural networks (HNN), adaptive neuro-fuzzy inference systems (ANFIS) or genetic algorithms (GA). Linear adaptive methods include algorithms based on the principles of Kalman filtering (KF), least mean squares filter (LMS), recursive least squares filter (RLS) or methods based on the principle of adaptive linear neuron (ADALINE) [9][10][11][12].…”

Section: Figurementioning

confidence: 99%

Hybrid In-Vehicle Background Noise Reduction for Robust Speech Recognition: The Possibilities of Next Generation 5G Data Networks

Martínek¹,

Baroš²,

Jaroš³

et al. 2022

Computers, Materials &Amp; Continua

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This pilot study focuses on employment of hybrid LMS-ICA system for in-vehicle background noise reduction. Modern vehicles are nowadays increasingly supporting voice commands, which are one of the pillars of autonomous and SMART vehicles. Robust speaker recognition for contextaware in-vehicle applications is limited to a certain extent by in-vehicle background noise. This article presents the new concept of a hybrid system, which is implemented as a virtual instrument. The highly modular concept of the virtual car used in combination with real recordings of various driving scenarios enables effective testing of the investigated methods of in-vehicle background noise reduction. The study also presents a unique concept of an adaptive system using intelligent clusters of distributed next generation 5G data networks, which allows the exchange of interference information and/or optimal hybrid algorithm settings between individual vehicles. On average, the unfiltered voice commands were successfully recognized in 29.34% of all scenarios, while the LMS reached up to 71.81%, and LMS-ICA hybrid improved the performance further to 73.03%.

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Section: Figurementioning

confidence: 99%