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Performance Comparison of Various Noisy Audio Signals Analysis Using Different Sampling Rates


Affiliations
1 ECE Department, PET Engineering College, Vallioor, Tamilnadu, India
2 Communication Systems, PET Engineering College, Vallioor, Tamilnadu, India
 

The discrete time systems that process data at more than one sampling rate are known as multirate systems. The two basic operations in multirate signal processing are decimation and interpolation.One of the important applications of multirate signal processing is sub-band coding of speech signal. In the proposed system, speech signal is taken as input signal. Additive White Gaussian Noise is added with the input speech signal. The input speech signal spectrum is divided into frequency sub-bands using a bank of finite response filters. Hamming, Hanning, Blackman, Rectangular and Kaiser windowing methods are used to implement the low pass and high pass filters. Finally performance of the proposed system is evaluated on the TIMIT data base using the parameters like leakage factor, main lobe width, side lobe attenuation, peak amplitude of side lobe and signal to noise ratio. The performance evaluation shows which window is suitable for designing the finite impulse response filters and sub-band coding system.

Keywords

Windowing, Signal to Noise Ratio, FIR Filter, Multirate.
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  • Performance Comparison of Various Noisy Audio Signals Analysis Using Different Sampling Rates

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Authors

P. Kannan
ECE Department, PET Engineering College, Vallioor, Tamilnadu, India
G. Bharatha Sreeja
ECE Department, PET Engineering College, Vallioor, Tamilnadu, India
J. Hermus Mary Shifani
Communication Systems, PET Engineering College, Vallioor, Tamilnadu, India

Abstract


The discrete time systems that process data at more than one sampling rate are known as multirate systems. The two basic operations in multirate signal processing are decimation and interpolation.One of the important applications of multirate signal processing is sub-band coding of speech signal. In the proposed system, speech signal is taken as input signal. Additive White Gaussian Noise is added with the input speech signal. The input speech signal spectrum is divided into frequency sub-bands using a bank of finite response filters. Hamming, Hanning, Blackman, Rectangular and Kaiser windowing methods are used to implement the low pass and high pass filters. Finally performance of the proposed system is evaluated on the TIMIT data base using the parameters like leakage factor, main lobe width, side lobe attenuation, peak amplitude of side lobe and signal to noise ratio. The performance evaluation shows which window is suitable for designing the finite impulse response filters and sub-band coding system.

Keywords


Windowing, Signal to Noise Ratio, FIR Filter, Multirate.

References