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Sheela Rani, B.
- Hardware Implementation of Fast Transversal Least Mean Square Algorithm in Acoustics, Speech, and Signal Processing (ASSP) Using TMS320C5X Processor
Abstract Views :166 |
PDF Views:3
Authors
Affiliations
1 Sathyabama University, Chennai-600119, IN
2 Sathyabama University, Chennai- 600119, IN
1 Sathyabama University, Chennai-600119, IN
2 Sathyabama University, Chennai- 600119, IN
Source
Digital Signal Processing, Vol 4, No 7 (2012), Pagination: 281-289Abstract
This paper describes the Normalized Least Mean Square algorithm and Fast Transversal Least Mean Square Algorithm for effective noise cancellation. The Simulink model for NLMS and Fast Transversal LMS Algorithm had been designed which results in a noise free signal as output in ASSP. The filter used here is adaptive filter and the algorithm used is Least Mean Square algorithm and Fast Transversal Least Mean Square Algorithm. The input given is the original speech signal/sinusoidal varying input where in white Gaussian noise / Random noise are deliberately introduced to the block. By varying the adaptive step size, Signal to Noise Ratio is determined and are compared for both the algorithms. Based on these results the optimum step size is found for noise free output and the best efficient algorithm is identified. The Fast Transversal LMS algorithm is found to be a suitable solution for adaptive filtering applications and hence chosen for implementation in hardware using TMS320C5X processor. Thus hardware has been implemented for effective removal of noise in audio and speech processing and it can be widely used in the detection of Narrow band signals in Broad band Noise.Keywords
Adaptive Filter, ASSP, FT-LMS and N-LMS, Tms320c5x Processor.
- Wavelet Transform Based De-Noising of ToFD Signals of Austenitic Stainless Steel Welds
Abstract Views :181 |
PDF Views:2
In this work, five austenitic stainless steel weldments were fabricated with different types of defects. The ToFD experiment is conducted on these welds. According to the characteristics of the resultant ToFD signals, discrete wavelet transform via different thresholdings are employed. Symlet and coiflet are chosen as mother wavelets. Various combination of wavelets, decomposition levels and different thresholdings are applied to find out the optimum denoising method. Finally evaluation of wavelet based denoising is achieved by calculating the SNR.
Results show that the noises can be suppressed well and SNR is improved. Symlet 4 with the 5th decomposition level in association with the hard thresholding is found as the effective signal denoising algorithm for all the 5 different types of defected ToFD signals.
Authors
Affiliations
1 Sathyabama University, Chennai, IN
2 IGCAR, Kalpakkam, IN
1 Sathyabama University, Chennai, IN
2 IGCAR, Kalpakkam, IN
Source
Digital Signal Processing, Vol 3, No 9 (2011), Pagination: 441-445Abstract
Non Destructive Evaluation is generally used for defect detection in welds. ToFD technique is one of the NDE methods, used in weld inspection to identify the weld defects. In ToFD testing, the quality of the signal is an important factor for identifying and classifying the defects. So, signal denoising is a key to successful application of ToFD testing. Many signal processing techniques are followed to improve the quality of the signal. Wavelet Transform is one of the effective signal processing techniques. This paper presents a denoising algorithm ,which is suitable for denoising the ToFD signal of an unknown weld defect.In this work, five austenitic stainless steel weldments were fabricated with different types of defects. The ToFD experiment is conducted on these welds. According to the characteristics of the resultant ToFD signals, discrete wavelet transform via different thresholdings are employed. Symlet and coiflet are chosen as mother wavelets. Various combination of wavelets, decomposition levels and different thresholdings are applied to find out the optimum denoising method. Finally evaluation of wavelet based denoising is achieved by calculating the SNR.
Results show that the noises can be suppressed well and SNR is improved. Symlet 4 with the 5th decomposition level in association with the hard thresholding is found as the effective signal denoising algorithm for all the 5 different types of defected ToFD signals.