A Noise Reduction Method Based on Linear Prediction with Variable Step-Size

Kawamura, Arata; Iiguni, Youji; Itoh, Yoshio

doi:10.1093/ietfec/e88-a.4.855

Cited by 25 publications

(14 citation statements)

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“…Many speech enhancement methods have been established in decades [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. These speech enhancement techniques can be classified to time domain methods and spectral domain methods.…”

Section: Introductionmentioning

confidence: 99%

Single Channel Speech Enhancement Techniques in Spectral Domain

Kawamura

Thanhikam

Iiguni

2012

ISRN Mechanical Engineering

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This paper presents single-channel speech enhancement techniques in spectral domain. One of the most famous single channel speech enhancement techniques is the spectral subtraction method proposed by S.F. Boll in 1979. In this method, an estimated speech spectrum is obtained by simply subtracting a preestimated noise spectrum from an observed one. Hence, the spectral subtraction method is not concerned with speech spectral properties. It is well known that the spectral subtraction method produces an annoying artificial noise in the extracted speech signal. On the other hand, recent successful speech enhancement methods positively utilize the speech property and achieve an efficient speech enhancement capability. This paper presents a historical review about some speech estimation techniques and explicitly states the difference between their theoretical background. Moreover, to evaluate their speech enhancement capabilities, we perform computer simulations. The results show that an adaptive speech enhancement method based on MAP estimation gives the best noise reduction capability in comparison to other speech enhancement methods presented in this paper.

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Section: Introductionmentioning

confidence: 99%

Single Channel Speech Enhancement Techniques in Spectral Domain

Kawamura

Thanhikam

Iiguni

2012

ISRN Mechanical Engineering

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“…By avoiding the conception of a generation model of speech signal (AR model), it was shown in [4] that the proposed algorithm had better performance than the conventional methods [2,3] with re spect to noise suppression capability. However, since this algo rithm requires the calculation of inverse matrix, and its compu tation complexity is much accompanied with the increment of the size of state vector/matrix, the reduction of the computational com plexity is substantially required.…”

Section: Introductionmentioning

confidence: 99%

Noise suppression algorithm using Kalman filter with reduced computational complexity

Tanabe

Furukawa

2010

2010 International Symposium on Intelligent Signal Processing and Communication Systems

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We propose a noise suppression algorithm using Kalman filter with colored driving source. The algorithm aims to achieve robust noise suppression with reduced computational complexity by modify ing the canonical state space models in [4]. The remarkable fea tures of the proposed algorithm are that it can be realized by 3 multiplications and that it has the better performances compared with [2-4] despite the reduction of computational complexity, us ing only the Kalman filter algorithm for the proposed canonical state space models with the colored driving source: (i) a vector state equation is composed of the only speech signal, and (ii) a scalar observation equation is composed of speech signal and ad ditive noise. We have confirmation of validity of the proposed canonical state space models with the colored driving source, and also show the effectiveness through numerical results and subjec tive evaluation results.

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“…In [7], we have already proposed a simple and robust noise suppression algorithm using only the Kalman filter theory. By avoiding the conception of a generation model of speech signal (AR model), it was shown in [7] that the proposed algorithm had better performance than the conventional method [4], [6] with respect to noise suppression capability. However, since this algorithm requires the calculation of inverse matrix, the conventional method requires high computational complexity by increasing the vector/matrix size.…”

Section: Introductionmentioning

confidence: 99%

A Kalman Filter based Fast Noise Suppression Algorithm

Tanabe

Furukawa

Tsujii

2009

2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop

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We have proposed a robust noise suppression algorithm with Kalman filter theory [7]. In this paper, we propose a fast noise suppression algorithm by modifying the canonical state space model in [7]. The algorithm aims to achieve robust noise suppression with reduced computational complexity without sacrificing high quality of speech signal. The remarkable features of the proposed algorithm are that it can be realized by 3 multiplications and that it has the same performances or better ones compared with [7] despite the reduction of computational complexity under the same environments, using only the Kalman filter algorithm for the proposed canonical state space model with the colored driving source: (i) a vector state equation is composed of the only speech signal, and (ii) a scalar observation equation is composed of speech signal and additive noise. We have confirmation of validity of the proposed canonical state space model with the colored driving source, and also show the effectiveness through numerical results and subjective evaluation results.

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A Noise Reduction Method Based on Linear Prediction with Variable Step-Size

Cited by 25 publications

References 0 publications

Single Channel Speech Enhancement Techniques in Spectral Domain

Single Channel Speech Enhancement Techniques in Spectral Domain

Noise suppression algorithm using Kalman filter with reduced computational complexity

A Kalman Filter based Fast Noise Suppression Algorithm

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