A Novel Approach for detection of the symptomatic patterns in the acoustic biological signal using Truncation Multiplier

Design of low power systems become a preeminent role in the domain of VLSI. Various algorithms are being used to implement a reliable system. This paper discusses the diagnosis of various symptoms in the audio signal. Discrete Wavelet Transform is the mathematical block for the detection and analysis of the spectral components of an audio signal. The wavelet transform detects signal levels in the acoustic signal. These coefficients are further applied to mathematical blocks such as coastline, quasi-average and energy blocks to diagnosis symptoms respectively depending on the frequency ranges. The symptoms such as cough, sneeze having similar spectral ranges which are distinguish using Mel spectrum based analysis. The aim is to diagnosis the symptomatic patterns in human audio signals with low power design and area efficient computational architecture for the mathematical blocks. The proposed work employs the truncation multiplier design in the wavelet transform and energy parameter. Since the LSB parts are not required in the realization of the wavelet transform filters which reduce the power and area consumption thereby makes the design a power and area efficient system. It is observed a significant change in the power consumption <; 90% in the implementation. The system is implemented using Xilinx 14.5, ModelSim for the functionality and analysis of power.