Application of the tuned Kalman filter in speech enhancement

Das, Orchisama; Goswami, Bhaswati; Ghosh, Ratna

doi:10.1109/cmi.2016.7413711

Cited by 7 publications

(4 citation statements)

References 9 publications

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“…Since the filter is a steady space model a n×1 state vector-matrix X(k) is to be obtained and equation 1.1 is rewritten in steady space model which is explained in [1] as equation 1.4.…”

Section: B Kalman Filtering Conceptmentioning

confidence: 99%

“…So these cases of practical interest filtering of noise is necessary. Kalman filter is largely MMSE estimator that predicts the unknown states of system [1]. There are quite a few algorithms proposed for enhancement of speech such as Spectral subtraction which subtracts an estimate of the average noise from the noisy signal spectrum [2].…”

Section: Introductionmentioning

confidence: 99%

“…Since then various methods have been proposed like iterative Kalman filtering [6]. The whole process of Kalman filter in speech enhancement was focused very recently in [1] where filter tuning was achieved very accurate and an self model-order determination system is proposed and using Kalman filter equations based on this order future states are estimated and noisy speech signal is filtered and using Correlation functions segmential SNR are obtained which are compared to various noises like white, train, babble. Many observations have been made in increasing SNR of Kalman filtered output and many researches are going on at present day to make more efficient Kalman filter.…”

Section: Introductionmentioning

confidence: 99%

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Removal of Pink Noise from Corrupted Speech Signal using Kalman Filter

Ahmed*,

Hota

2020

IJITEE

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Speech enhancement has been a major challenge in the field of Signal processing. The process of filtering the noise component from the speech signal has achieved many milestones since the early 20th century. Beside many theories Linear prediction coding is one of the best methods for speech, audio signal processing which uses the algorithm of predicting the current estimates based on the past states of an LTI system. Linear prediction is usually used in Speech recognition, Speech enhancement. One of such Kalman filter was introduced and described in 1960 by Rudolf Kalman, which uses the concept of linear quadratic estimation. Kalman filtering is effectively being used in the practical applications like navigation of ships or aircraft, designing motion planning algorithms, in communication area. Kalman filters use the autoregression model of speech for the recursive equations of Kalman filter used in state space model of filter for state estimation. In this paper, we have used Kalman filter to eliminate the pink noise from the corrupted speech signal. Pink noise is very common in electronic devices and occurs in almost all devices. The Speech corrupted with pink noise has been obtained from SpEAR database. We have used MATLAB software for the simulation purpose. Finally, Spectrograms of signals are plotted for a better visual understanding of filtered results.

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“…Since the filter is a steady space model a n×1 state vector-matrix X(k) is to be obtained and equation 1.1 is rewritten in steady space model which is explained in [1] as equation 1.4.…”

Section: B Kalman Filtering Conceptmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Removal of Pink Noise from Corrupted Speech Signal using Kalman Filter

Ahmed*,

Hota

2020

IJITEE

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show abstract

“…Apart, these methods employed filtering techniques which can be viewed as an aspect of enhanced quality. In like manner, the noise corrupted vocal signing clip is cleaned with tuned Kalman filter (Das et al 2016). Thereby, Kalman filtering technique is highly preferred by considering the presence of non-linear noise such that instantaneous state in a linear dynamic system is injected by noise at lower SNRs (Sorqvist et al 1997).…”

Section: Introductionmentioning

confidence: 99%

Spectral-Warping Based Noise-Robust Enhanced Children ASR System

Bawa

Kadyan

Kumar

et al. 2021

Preprint

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In real-life applications, noise originating from different sound sources modifies the characteristics of an input signal which affects the development of an enhanced ASR system. This contamination degrades the quality and comprehension of speech variables while impacting the performance of human-machine communication systems. This paper aims to minimise noise challenges by using a robust feature extraction methodology through introduction of an optimised filtering technique. Initially, the evaluations for enhancing input signals are constructed by using state transformation matrix and minimising a mean square error based upon the linear time variance techniques of Kalman and Adaptive Wiener Filtering. Consequently, Mel-frequency cepstral coefficients (MFCC), Linear Predictive Cepstral Coefficient (LPCC), RelAtive SpecTrAl-Perceptual Linear Prediction (RASTA-PLP) and Gammatone Frequency cepstral coefficient (GFCC) based feature extraction methods have been synthesised with their comparable efficiency in order to derive the adequate characteristics of a signal. It also handle the large-scale training complexities lies among the training and testing dataset. Consequently, the acoustic mismatch and linguistic complexity of large-scale variations lies within small set of speakers have been handle by utilising the Vocal Tract Length Normalization (VTLN) based warping of the test utterances. Furthermore, the spectral warping approach has been used by time reversing the samples inside a frame and passing them into the filter network corresponding to each frame. Finally, the overall Relative Improvement (RI) of 16.13% on 5-way perturbed spectral warped based noise augmented dataset through Wiener Filtering in comparison to other systems respectively.

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Speech Background Noise Removal Using Different Linear Filtering Techniques

Haque

Bhattacharyya

2018

Lecture Notes in Electrical Engineering

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Application of the tuned Kalman filter in speech enhancement

Cited by 7 publications

References 9 publications

Removal of Pink Noise from Corrupted Speech Signal using Kalman Filter

Removal of Pink Noise from Corrupted Speech Signal using Kalman Filter

Spectral-Warping Based Noise-Robust Enhanced Children ASR System

Speech Background Noise Removal Using Different Linear Filtering Techniques

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