Research
Speech Enhancement using Bolls Spectral Subtraction Method
This paper investigates the effect of Gaussian window frequency response Side lobe Attenuation on the improvement of Speech quality in terms of six objective quality measures. In Speech Enhancement process, signal corrupted by noise is segmented into frames and each segment is Windowed using Gaussian window with variation in the side lobe attenuation parameter “αâ€. The Windowed Speech segments are applied to the Boll’s Spectral Subtraction Speech Enhancement algorithm and the Enhanced Speech signal is reconstructed in its time domain. The focus is to investigate the effect of Gaussian window frequency response side lobe level on the Boll’s Spectral Subtraction Speech enhancement. For various side lobe attenuations of the Gaussian window frequency response, speech quality objective measures have been computed. From this study, it is observed that the Side lobe Attenuation parameter “α†plays an important role on the Speech enhancement process in terms of six objective quality measures. The results are compared with the measures of Hamming window and an optimum side lobe attenuation parameter value for the Gaussian window is proposed for better speech quality.
Implementation of K-means Clustering Algorithm using Java
Emergence of modern techniques for scientific data collection has resulted in large scale accumulation of data pertaining to diverse fields. Conventional database querying methods are inadequate to extract useful information from huge data analysis. Cluster analysis is one of the major data analysis methods and k-means clustering algorithm Emergence of modern techniques for scientific data collection has resulted in large scale accumulation of data pertainting diverse felids. Conventional Data base methods are inadequate to extract useful information from huge data banks. Cluster analysis is one of the major data analysis methods and the k-means clustering algorithm is widely used for many practical applications. But the original k-means algorithm is computationally expensive and the quality of the resulting clusters heavily depends on the selection of initial cancroids. Several methods have been proposed in the literature for improving the performance of the k-means clustering algorithm. The k-means algorithm is computationally expensive and requires time proportional to the product of the number of data items, number of clusters and the number of iterations.This papert proposes a method for making the algorithm more effective and efficient.
Improve Speech Enhancement Using Weiner Filtering
Speech enhancement aims to improve speech quality by using various algorithms. It may sound simple, but what is meant by the word quality. It can be at least clarity and intelligibility, pleasantness, or compatibility with some other method in speech processing. Wiener filter are rather simple and workable, but after the estimation of the background noise, one neglects the fact that the signal is actually speech. Furthermore, the phase component of the signal is left untouched. However, this is perhaps not such a bad problem; after all, human ear is not very sensitive to phase changes. The third restriction in spectral subtraction methods is the processing of the speech signal in frames, so the Proceeding from one frame to another must be handled with care to avoid discontinuities. Noise reduction is a key-point of speech enhancement systems in hands-free communications. A number of techniques have been already developed in the frequency domain such as an optimal short-time spectral amplitude estimator proposed by Ephraim and Malah including the estimation of the a priori signal-to-noise ratio. This approach reduces significantly the disturbing noise and provides enhanced speech with colorless residual noise. In this paper, we propose a technique based on a Wiener filtering under uncertainty of signal presence in the noisy observation. Two different estimators of the a priori signal-to-noise ratio are tested and compared. The main interest of this approach comes from its low complexity. In this paper we demonstrate the application of weiner filter for a speech signal using Matlab 7.1 and signal processing toolbox.
