Learning Experience of Digital Signal Processing and Processors Laboratory

           Real-Time speech recognition system is essential for the hands-free applications, this paper did the hardware implementation of real-time speech recognition system, by two approaches and their performances are evaluated. The first approach uses Mel Filter Banks with Mel Frequency Cepstrum Coefficients (MFCC) as feature input and the second approach uses Cochlear Filter Banks with Zero-crossings (ZC) as feature input for recognition.Recognition systems are implemented on a Texas Instruments TMS320C6713 floating point digital signal processor.The recognition performance is also studied for different combinations of test and training.The procedure is 1. sampling 2.End point detection 3.MFCC/Cochlear 4.SVM classifier 5.Recognized word.According to authors, The proposed real-time speech recognition system can be easily employed for various hands-free applications. Paper- Hardware Implementation of Real-Time Speech Recognition System using TM...

In this Laboratory session, the aim was to perform the basic operations on DSP Processor specifically on Texas Instruments C2000 DSP(Digital Signal Processor) TMS320F28335.  The platform was Code Composer Studio 3 where the code was written in C language.The result various registers was noted down before and after each execution which is the major difference while performing operations on DSP board and the C compiler.  The output of these instructions is seen using the JTAG debugger. DSP Processor allows faster and more efficient execution of this instruction with the help of Multiply and Accumulate Unit (MAC). Basic operations were performed on the C2000 Board such as addition and subtraction.We have also performed the Shift operations on DSP board

frequency Sampling Method is an another way of designing linear phase FIR(Finite Impulse Response) Filter. The process of obtaining the desired frequency response is same as the window function method. The desired frequency response is sampled in the frequency domain,  Discrete Fourier Transform(DFT) of samples was taken. FIR filter response is now obtained by taking Inverse Discrete Fourier Transform(IDFT). The results were verified by comparing the stopband and passband attenuation from the magnitude and phase spectrum which were plotted on the Graphic window of the Scilab.

The aim of the experiment was to design linear phase FIR (Finite Impulse Response) filter using window function.In this experiment, we have used hamming window function to design Low pass filter (LPF) and high-pass fiter(HPF)  Bandpass/band stop filter. We have plotted magnitude as well as phase response. There are  different types of windowing functions Rectangular, Bartlett, Hamming, Hanning, and Blackman.The specifications of As, Ap, stop band frequency, passband frequency, and sampling frequency was taken from the user.It is observed that as the order of the filter increases the number of lobes in frequency response increase.The attenuation depends upon the type of window used.  The phase response of the filter is linearly varying with frequency and no distortion is observed in the output.

   In the last Experiment, we have performed the Butterworth filter. The aim was to design Chebyshev filter, Chebyshev filters are filters which have ripple in their frequency response. Filters having ripple in the passband and monotonic in stopband are called Chebyshev-1 filters. Whereas, filters having ripple in their stopband and monotonic in passband are called Chebyshev-2 filters. The design procedure was again on Scilab.We can conclude from the experiment that,  Poles of the Chebyshev filter lie on ellipse where poles of Butterworth filter lie on the unit circle.Order of Chebyshev filter is less than the order of Butterworth filter and thus realization of Chebyshev filter requires less hardware.  

  A filter is a very important part of the system, so the task was to design the Butterworth filter . This experiment was performed using Scilab which is the open source alternative to MATLAB.We have designed both filters i.e.High Pass Filter and Low Pass Filter using Scilab. Here the parameters like passband attenuation (Ap), stopband attenuation (As), analog passband frequency (Fp), analog stopband frequency (fs) and sampling frequency (Fs).were taken as inputs. Sampling frequency selected almost 4 times higher than the highest frequency parameter,  for LPF Fp<Fs<fc and for HPF Fs<Fp<Fc  Based on these parameters, a digital Butterworth filter (LPF and HPF) was designed using Bi-Linear Transformation(BLT) Method. Once, the digital filter was designed, order and transfer function were displayed and magnitude response of the filter was shown in the Scilab Graphic window.