Data Acquisition System Design Using a Microcontroller and Digital Elliptic Filters that are able to Remove Noise from EEG signals

Since Electroencephalogram (EEG) signals have very small magnitude, it's very hard to capture these signals without having noise (produced by surrounding artifacts) affect the real EEG signals, so it is necessary to use Filters to remove noise. This work proposes a design of an electronic circuit using a microcontroller, an instrumentation amplifier and an operational amplifier able to capture EEG signals, convert the captured signals from analog state to digital one and send the converted signal (digital signal) to a group of three digital filters. This paper gives a design of three digital elliptic filters ready to be used in real time filtering of EEG signals (which preliminary represents the condition of the brain) making the software part which complements the hardware part in the EEG signals capturing system. Finally we are going to show the way of using the designed electronic circuit with the three designed digital filters, demonstrate and discuss the results of this work. We have used Eagle 6.6 software to design and draw the circuit, CodeVision AVR 3.12 software to write the program downloaded on the microcontroller, Mathworks MATLAB 2014a software to design the three digital filters and Mathworks MATLAB 2014a Simulink tool to make the appropriate experiments and get the results.

Cepstral Vocal Tract Modelling for Text-To-Speech Synthesis

In this paper we describe a cepstral model of the vocal tract which models both formants and antiformants. The investigated model is more precise compared to the linear prediction model, which models only the formants of the vocal tract. The exponential function is used for the inverse transformation. However, it is difficult to implement this function on a digital signal processor. To solve this issue we use a continued fraction expansion to approximate the exponential function. The transfer function that approximates the exponential function is realized by using the Infinite Impulse Response (IIR) digital filter, in which branches type Finite Impulse Response (FIR) digital filters are included. The coefficients of the FIR digital filters are just the coefficients of the real speech cepstrum. The state-space difference equations are proposed and implemented on a DSP56300 fixed-point digital signal processor (Motorola). Finally, the results of the digital signal processor implementation for chosen vowels and consonants are evaluated.