Digital Signal Processing

R. Ali ¹, O. Zahran ¹, M. El-Kordy ¹, F. E. Abd El-Samie ¹

Affiliations
1 Department of Electronics and Electrical Communication, Menoufia University, Menouf, EG

Abstract

This paper addresses the problem of blind signal separation (BSS) for the system of multiple input and multiple output signals (MIMO). We use different modulation techniques such as quadrature phase shift keying (QPSK), minimum shift keying (MSK), and Gaussian minimum shift keying (GMSK). Several methods have been used to solve this problem such as principle component analysis (PCA), independent component analysis (ICA), and multi user kurtosis (MUK) algorithms. We use different modulation techniques and different algorithms in the separation to compare between results and take into consideration the good separation. In this paper, we propose wavelet denoising with PCA, ICA and MUK methods. We consider the instantaneous mixture of two sources. The simulation results show a considerable improvement in extracted signals when compared to original signals. We assume mean square error (MSE) between extracted and original signals to compare between them to give the better result. 

Keywords

BSS, MIMO, PCA, ICA, MUK, MSK, GMSK, QPSK, MSE.

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R. Ali ¹, O. Zahran ², M. Elkordy ³, F. E. Abd El-Samie ²

Affiliations
1 Department of Electronics and Electrical Communications, Menoufia University, Menouf, EG
2 Department of Electronics and Electrical Communications, Menoufia University, Menouf, EG
3 Department of Electronics and Electrical Communications, Menoufia University, Menouf, EG

Abstract

This paper studied the problem of blind signal separation (BSS) for the system of multiple input and multiple output signals (MIMO) of noisy signals. It uses the separation algorithm for the discrete cosine transforms (DCT) for blind of mixed signals, instead of separating the mixtures themselves, as a technique that achieves a great result in eliminating the noise.  We used the proposed algorithm with multi user kurtosis (MUK) that used for blind of mixed signals. We consider the instantaneous mixture of two sources. The simulation results show a considerable improvement in extracted signals when compared to original signals. We assume signal to noise ratio (SNR) between extracted and original signals to compare between them to give the better result. The separation of signals in a noisy environment is studied with and without the using the new technique. The simulation results confirm the usefulness of this technique.

Keywords

BSS, MIMO, MUK, SNR.

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S. Abd El-Moneim ¹, M. A. Nassar ², M. Abdelnaby ², F. E. Abd El-Samie ³

Affiliations
1 Department of Electronics and Electrical Communications, Faculty of Electronic Engineering. Menoufia University, Menouf, 32952, EG
2 Department of Electronics and Electrical Communications, Faculty of Electronic Engineering. Menoufia University, Menouf, 32952, EG
3 Department of Electronics and Electrical Communications, Menoufia University, Menouf, 32952, EG

Abstract

Speech signal is a signal that conveys information about the identity, words, age and emotional state of the speaker. If this signal is corrupted by noise, this information may be lost or become difficult to hear.There are several speech enhancement methods such as spectral subtraction method, Wiener filter method and adaptive wiener filtering method. A proposed method of speech enhancement to reduce the degradation of speech signal is introduced in this paper depending on speech averaging, median filtering or minimum periodogram. This averaging method can be used as a preprocessing step in speaker identification. Simulation results show a good performance of the proposed speech enhancement method.

Keywords

Speech Recognition, Speech Enhancement, Speech Averaging, Spectral Subtraction,Wiener Filter, Adaptive Wiener Filter, Speech Quality Metrics.

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Hala Shawky ¹, A. Nassar ², M. Abdelnaby ², F. E. Abd El-Samie ³

Affiliations
1 Department of Electronics and Electrical Communication, Menouf University, Menouf, IN
2 Department of Electronics and Electrical Communication, Menouf University, Menouf, EG
3 Department of Electronics and Electrical Communication, Menouf University, Menouf, EG

Abstract

Speech compression is a process of compressing speech signal to reduce its size for transfer. This paper proposed a new technique to compress the speech signal. This technique is called the decimation process. It is opposite of interpolation. This process reduces the sampling rate and thus save time, storage capacity, and cost. Decimation contains two stages, processes of lowpass filtering followed by downsampling. The benefit of using a filter is to avoid aliasing effect. The reconstruction of the original speech signal can be performed using inverse interpolation techniques such as maximum entropy and regularization theory. Finally, we assess the quality of the reconstructed signal using quality metrics such as signal-to-noise ratio (SNR), signal to noise ratio segmental (SNRseg), spectral distortion (SD) and log-likelihood ratio (LLR).

Keywords

Decimation, Interpolation, Maximum Entropy, Regularisation Theory.

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Mohamed M. Al-Abasy ¹, M. I. Dessouky ², M. Abdelnaby ³, F. E. Abd El-Samie ²

Affiliations
1 Department of Electronics and Electrical Communications, Menoufia University, Menouf, 32952, EG
2 Department of Electronics and Electrical Communications, Menoufia University, Menouf, 32952, EG
3 Department of Electronics and Electrical Communications, Menoufia University, Menouf, 32952, EG

Abstract

The importance of seismic wave researches concentrate of the ability to predict earthquake and also obtain information on the Earth’s composition and features. Compression is the reduction of signal size to save storage capacity, transmission time, and cost. In this paper, we propose a technique for seismic signal compression and two techniques for seismic signal reconstruction. Compression is performed with a decimation process, which reduces the sampling rate. The reconstruction of the original seismic signal can be performed using inverse interpolation techniques such as maximum entropy and regularization theory. In addition, we apply compression through multiplication of a certain non-square matrix followed by a pseudo-inverse process for reconstruction. Finally we assess the quality of the reconstructed signals using quality measures such as Signal-to-Noise Ratio (SNR), segmental Signal-to-Noise Ratio (SNRseg), Spectral Distortion (SD), and Log-Likelihood Ratio (LLR).

Keywords

Seismic Signal, Decimation, Maximum Entropy, Regularization Theory.

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