Speech enhancement using Kalman Filter for white, random and color noise

Mathe, Mariyadasu; Nandyala, Siva Prasad; Kumar, T. K. Satish

doi:10.1109/icdcsyst.2012.6188703

Cited by 18 publications

(4 citation statements)

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“…Many different algorithms have been proposed to estimate the speech model parameters, such as using expectationmaximisation algorithm [4,5], subspace non-iterative algorithm based on orthogonal projection in [6], log-spectral amplitude minimum-mean-square-error (MMSE) in [7], power spectral subtraction method in [8,9], comparison to a masking threshold that computed from both time and frequency-domains simultaneous masking properties of human auditory systems in [10], estimation of the clean-speech short-term predictor parameters from noisy speech using maximum-a-posteriori and MMSE techniques in [11]. Besides, the use of the Kalman filter in the speech recognition system to improve the speech recognition accuracy has been proposed in [12,13], Mathe et al [14] have used the Kalman filter for speech enhancement purpose.…”

Section: Introductionmentioning

confidence: 99%

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Robust speech recognition system using bidirectional Kalman filter

Goh

Raveendran

Goh

2015

IET signal process.

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Kalman filter is normally used to enhance speech quality in a noisy environment, in which the speech signals are usually modelled as autoregressive (AR) process, and represented in the state-space domain. It is a known fact that to identify the changing AR coefficients in every time state requires extensive computation. In this paper, the authors develop a bidirectional Kalman filter and apply it in a speech processing system. The proposed filter uses a system dynamics model that utilises the past and the future measurements to form an estimate of the system's current time state. It provides efficient recursive means to estimate the state of a process that minimises the mean of the squared error. Compared to the conventional Kalman filter, the proposed filter reduces the computation time in two ways: (i) by avoiding the computation of AR parameters in each time state, and (ii) by reducing the dimension of the matrices involved in the difference equations and the measurement equations into constant (1 × 1) matrices. The speech recognition result shows that the developed speech recognition system becomes more robust after the proposed filtering process, and the proposed filter's low computational expense makes it applicable in the practical hidden Markov model-based speech recognition system.

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Section: Introductionmentioning

confidence: 99%

“…Besides, the use of the Kalman filter in the speech recognition system to improve the speech recognition accuracy has been proposed in [12, 13], Mathe et al . [14] have used the Kalman filter for speech enhancement purpose.…”

Section: Introductionmentioning

confidence: 99%

Robust speech recognition system using bidirectional Kalman filter

Goh

Raveendran

Goh

2015

IET signal process.

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“…The presence of background noise in speech significantly reduces the intelligibility of speech. Degradation of speech severely affects a person's ability, whether impaired or normal hearing, to understand what the speaker is saying [7]. Recent researches have developed a variety of theoretical methods to fight this problem, but the complexity of this task is always linked to the random nature of the noise and the inherent complexities of speech.…”

Section: Introductionmentioning

confidence: 99%

“…This paper presents a method for speech enhancement based on a dynamic smart Gaussian filter. Our aims is to remove real-colored noise which have, unlike white Gaussian noise (WGN), a spectrum that is not flat [7]. Therefore, the noise does not affect the speech signal uniformly over the whole spectrum.…”

Section: Introductionmentioning

confidence: 99%

Efficient speech de-noising applied to colored noise based dynamic low-pass filter supervised by cascade neural networks

Selmani¹,

Seddik²,

Mbarki³

2013

2013 International Conference on Electrical Engineering and Software Applications

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In this paper, we investigated the enhancement of speech by applying an optimal adaptive low-pass filter supervised by neural network. The corruption of speech due to the presence of additive noise causes its degradation in quality and intelligibility. To filter this distorted signal in its spatial representation is a hard task. This task is more difficult to realize if the distortion are caused by colored noise. In addition using a static filter is not efficient due to the speech signal variability. In the same sentence a phoneme can change in shape and amplitude. For these constraints, we propose to apply a low-pass filter with Gaussian core supervised by neural networks. Filtering strength changes continuously with the phoneme variation to generate a variable filter that change over the whole sentence.

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