Design of a Low Power and Area Efficient Architecture for the Detection of Audio Biological Signals

Abstract

In digital audio recording the audio signals are picked by a microphone or other transducer and converted into a stream of discrete numbers, representing the changes over time in air pressure for audio, then recorded to a storage device. Hence these audio recordings are analyzed to detect the audio biological symptoms, such as cough, sneeze, vomiting, wheezing, belching and so on, which are spectrally analyzed using a discrete wavelet transform (DWT). The DWT will help to find out the signal level of variation and also use simple mathematical metrics, such as energy, quasi-average, and coastline parameters. These parameters are used to find out type of symptomatic patterns to be detected. Furthermore a Mel-frequency cepstrum-based analysis is applied to distinguish between signals, such as cough and sneeze, which have similar frequency response and hence occur in common wavelet coefficients. The proposed approach is to detect the symptomatic patterns using acoustic non speech human signals which increases the efficiency of mathematical metrics and in particular reduces the area occupied by the architecture. Thus the aim of the proposed work is to design a low power and area efficient mathematical architecture for the calculation of energy parameter, coastline parameter, quasi-average and Mel Cepstrum based analysis for the detection of different symptomatic patterns in audio biological signals. Existing method uses Binary Common Sub-Expression Elimination (BCSE) technique in the design of multiplier which is used in the architecture of DWT and energy parameter calculation. The proposed work employs Multiple Constant Multiplier (MCM) technique in the design of DWT and energy parameter calculation. The design based on MCM outperforms the design using common sub-expression elimination technique with respect to hardware resources and power consumption thereby making the design a low power and area efficient.