Dual-Channel Speech Enhancement Based on Extended Kalman Filter Relative Transfer Function Estimation
Metadatos
Afficher la notice complèteAuteur
Martín Doñas, Juan M.; Peinado Herreros, Antonio Miguel; López Espejo, Iván; Gómez García, Ángel ManuelEditorial
Applied sciences
Materia
dual-microphone smartphone beamforming relative transfer function speech presence probability postfiltering
Date
2019-06-20Referencia bibliográfica
Martín-Doñas, J. M., Peinado, A. M., López-Espejo, I., & Gomez, A. (2019). Dual-Channel Speech Enhancement Based on Extended Kalman Filter Relative Transfer Function Estimation. Applied Sciences, 9(12), 2520
Patrocinador
Spanish MINECO/FEDER Project TEC2016-80141-P; Spanish Ministry of Education through the National Program FPU under Grant FPU15/04161Résumé
This paper deals with speech enhancement in dual-microphone smartphones using
beamforming along with postfiltering techniques. The performance of these algorithms relies on
a good estimation of the acoustic channel and speech and noise statistics. In this work we present
a speech enhancement system that combines the estimation of the relative transfer function (RTF)
between microphones using an extended Kalman filter framework with a novel speech presence
probability estimator intended to track the noise statistics’ variability. The available dual-channel
information is exploited to obtain more reliable estimates of clean speech statistics. Noise reduction
is further improved by means of postfiltering techniques that take advantage of the speech presence
estimation. Our proposal is evaluated in different reverberant and noisy environments when the
smartphone is used in both close-talk and far-talk positions. The experimental results show that our
system achieves improvements in terms of noise reduction, low speech distortion and better speech
intelligibility compared to other state-of-the-art approaches.