Digital Signal Processing

Kapil Belpatre ¹, M. R. Bachute ², R. D. Kharadkar ³

Affiliations
1 Someshwarnagar, Pune,Maharashtra, IN
2 G. H. Raisoni Institute of Engineering and Technology, Pune, Maharashtra, IN
3 G. H. Raisoni Institute of Engineering and Technology, Pune, Maharashtra, IN

Abstract

Speech Enhancement deals with improvement of quality of speech signal corrupted by additive noise. For this purpose different methods are available out of which this paper deals with use of different algorithms in Adaptive Filter. It is a primary method to filter noise signal, because it does not need the signal statistical characteristics. In many applications for e.g. speech recognition, speaker identification and noise cancellation. Out of that noise cancellation is great challenge, as changes in speech characteristics could be quite fast. Thus adaptive algorithms require the utilization that converges rapidly is required. Now, in this case we deal with implementation of different Adaptive algorithms for improvement of degraded signal. Different parameters such as Mean Square Error, Computational time and the most important thing is Signal to Noise ratio is considered. It has been observed that the performance of different implemented as well as modified algorithms is better with respect to each of different parameters such as MSE, SNR & speed .In this paper we have compared different existing algorithms & newly implemented algorithms.

Keywords

Least Mean Squares (LMS), Mean Square Error (MSE), Normalized Least Mean Squares (NLMS), Recursive Least Square (RLS), Signal to Noise Ratio (SNR).

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S. V. Gurao ¹, M. R. Bachute ²

Affiliations
1 G.H. Raisoni Institute of Engg. & Tech., Pune, IN
2 Dept. of Electronics & Telecom, G.H. Raisoni Institute of Engg. & Tech., Pune, IN

Abstract

Speech enhancement in a real life working environment is a challenging open problem, which remains unsolved after a long research. The main difficulty towards enhancement is different environmental conditions as well as speech enhancement is processed in real time basis. In this paper a new approach towards speech enhancement is proposed using EM algorithm and Hidden markov model (HMM) model. This unique approach gives information about statistical structure of speech signal using speech model. By using this new technique a speech model is parameterized by the coefficient of the reverberation filter and spectra of the sensor noise signal. In new approach EM algorithm and HMM model work together, so that noiseless speech signal is clearly audible user. The main function of EM algorithm is that to estimate Noise parameter from Hidden variable model and develop a Bayes optimal model of the original speech signal, At the same time reduces the magnitude of noise signal. This new technique of speech enhancement is having better solution over the traditional methods like Spectral subtraction, Array processing, Noise cancellation. EM algorithm and HMM model work together so we are getting good values of performance parameter as compare to earlier methods. The important parameter for speech enhancement is signal to noise ratio (SNR). So higher the SNR value, noise is less. And result is enhanced speech signal which is noise free.

Keywords

Speech Enhancement, EM Algorithm, HMM Model, Probabilistic Model Approach.

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Sneha S. Pathade ¹, S. D. Lokhande ¹, M. R. Bachute ²

Affiliations
1 Sinhgad College of Engineering, Pune, IN
2 G.H.R.I.E.T, Pune, IN

Abstract

In many application of noise cancellation, the changes in signal characteristics could be quite fast. This requires the utilization of adaptive algorithms, which converge rapidly. Least Mean Squares (LMS) and Recursive Least square (RLS) adaptive filters have been used in a wide range of signal processing application because of its simplicity in computation and implementation. Unfortunately, practical implementations of the algorithm are often associated with high computational complexity and/or poor numerical properties. In this describes a new approach for noise cancellation in speech enhancement using the two new adaptive filtering algorithms for attenuating noise in speech signals. Three performance criteria are utilized in this study: minimum step size, the minimum mean squared error (MSE), and the required filter order. The simulation results demonstrate the good performance of the LMS, RLS, and filtered-X LMS algorithm for active noise cancellation. After simulation in MATLAB the filtered-X algorithm is implemented on the TMS320C6713 processor to see the performance by use of speaker.

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