Kalman filter with linear predictor and harmonic noise models for noisy speech enhancement

Qin Yan, Saeed V. Vaseghi, Esfandiar Zavarehei, Ben P. Milner

Research output: Contribution to conferencePaper

1 Citation (Scopus)


This paper presents a method for noisy speech enhancement based on integration of a formant-tracking linear prediction (FTLP) model of spectral envelope and a harmonic noise model (HNM) of the excitation of speech. The time-varying trajectories of the parameters of the LP and HNM models are tracked with Viterbi classifiers and smoothed with Kalman filters. A frequency domain pitch estimation is proposed, that searches for the peak SNRs at the harmonics. The LP-HNM model is used to deconstruct noisy speech, de-noise its LP and HNM models and then reconstitute cleaned speech. Experimental evaluations show the performance gains resulting from the formant tracking, harmonic extraction and noise reduction stages.
Original languageEnglish
Publication statusPublished - Sep 2006
Event14th European Signal Processing Conference - Florence, Italy
Duration: 4 Sep 20068 Sep 2006


Conference14th European Signal Processing Conference
Abbreviated titleEUSIPCO 2006

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