Basic operations on DSP processor

The aim of this experiment is to perform basic DSP instructions on DSP processor TMS320F28375. Various operations were performed on the DSP processors, Operations included addition, multiplication, subtraction, etc.  We noted and verified the result stored in the registers before and after execution of a particular instruction.

Design of FIR filter using window method

In this experiment, we designed Linear phase FIR filter using window functions and observed their spectrum of the filter. Depending upon the value of Passband attenuation, appropriate window function was chosen from Rectangular, Bartlett, Hanning, Hamming, and Blackman.We used a Hanning Window as the window function and wrote the code accordingly. In this experiment, the value of As and Ap are verified. As we go on increasing As depending on the filter the side lobe width decreases & main lobe width increases.  Phase response varies linearly with frequency, thus no distortion is observed at the output of the filter.

FIR Filter using Frequency Sampling Method

In this experiment, we designed a digital FIR filter using Frequency Sampling Method (FSM) using Scilab. We plotted the Magnitude and phase spectrum for two cases of LPF and HPF. By taking the input specification we designed high pass and low pass filter & we observed the phase as well as magnitude spectrum.  We noticed that discontinuity in phase plot between lobes and when the phase spectrum goes out of the range of -pi to pi. Phase varies linearly with frequency, hence output will not be distorted.

Paper Review

This was a group Experiment - finding out relevant papers and patents on DSPP applications. We, as a group of 5 - Abhijit Haridas, Shreyas Padte, Vipan Koul, Sakshi Joshi and myself had to study on research papers and patents which implemented compression of the audio signal. We chose to study "Audio Compression" as an application.

Paper Review

Paper: International Journal of Computer Applications (0975 – 8887)                                                                 Volume 86 – No 13, January 2014                                         

Summary:

Audio compression has been carried out by various methods such as Discrete Cosine Transform, Wavelet Transform, Wavelet Packet Transform (W.P.T) & Cosine Packet Transform. Mean compression ratio has been calculated for each of above methods. Various other parameters have been calculated and compared. Output for each transformation was observed and it was found that Wavelet Packet Transform gives the best compression ratio as compared to other methods.

Paper link: https://mail.google.com/mail/u/0/#sent/15b57a059d75a6c4?projector=1 

Plagiarism link: https://drive.google.com/open?id=0B06hNCrbOenyVHZtRVl5Zkctd2s

Patent review

This was a group Experiment - finding out relevant papers and patents on DSPP applications. We, as a group of 5 - Abhijit Haridas, Shreyas Padte, Vipan Koul, Sakshi Joshi and myself had to study on research papers and patents which implemented compression of the audio signal. We chose to study "Audio Compression" as an application.

Patent Review

Patent No: US 20030220801 A1                                                                                                             Publication date: Nov27,2003                                                                                                                   Inventor: Thomas Spurrier           

Summary:

Compression is a signal processing operation that reduces the noise or amplifies quiet sounds by compressing an audio signal’s range. This method shows about compressed audio into a data stream that can be transmitted over any digital medium thus eliminating the need for higher levels of protocol. The signal is stored in a memory and compressed to find the maximum and minimum value and a respective number of samples are calculated between this range. BPF is used to the input for smooth and elimination of noise thus increasing the compression. The output of compressed signal is connected to a transmission channel and can observe on the receiver.

Patent link: https://www.google.com/patents/US20030220801

Design of Chebyshev Filter

In this experiment we used the same technique as we did for butterworth filter. We performed this experiment on SCILAB and same input parameters were given as in butterworth filter for LPF and HPF.
From this experiment we observed that, ripples were present in passband and on the other hand it was monotonic in stopband for LPF and it was vice versa for HPF.For LPF we get exact zero at -1 and for HPF we get exact zero at +1.

Design of Butterworth Filter

In this experiment, we designed a butterworth filter using SCILAB for implementation of the code.Input parameters such as As,Ap,Fs,Fp and F were used to design the filters.With the help of this parameters order of the filter,cutoff frequency,normalized filter and denormalized filter was calculated.
Graph was observed between Mag(in db) vs Frequency (in Hz).From the output we observed that the magnitude response is monotonic and poles lies inside unit circle which is the condition of stablilty.We used BLT method for design of filter,