Signal Processing Application

This was a group Experiment - finding out relevant papers and patents on DSPP applications. We, as a group of 5 - Abhijit Haridas, Shreyas Padte, Rishi Gupta, 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 20160344356 A1                                                                                                                        Publication date: Nov 24, 2016                                                                                                                                            Inventors: Peter Grosche, Yue Lang, Qing Zhang           

Summary:

This is the audio compression system comprising of a digital filter for filtering the input audio signal, where the digital filter comprises of a frequency transfer function having a magnitude over frequency, where the magnitude is formed by an equal loudness curve of a human ear to obtain a filtered audio signal, and a compressor which is configured to compress the input audio signal upon the basis of the filtered audio signal to obtain a compressed audio signal.

Patent link: https://www.google.com/patents/US20160344356

Paper Review

Paper: International Journal of Engineering and Innovative Technology ISO 9001:2008 Certified                                                                                                                                                                   Volume 4, Issue 1, July 2014

Summary:

Audio compression has been carried out by various methods such as Discrete Cosine Transform, Run Length and High order shift encoding. Audio compression addresses the problem of reducing the amount of data required to represent digital audio. Also to overcome the effect of quantized noise, which can be noticed at low energized audio segments, a post processing filtering stage is introduced as last stage of decoding process.

Paper link: http://www.ijeit.com/Vol%204/Issue%201/IJEIT1412201407_08.pdf

Plagiarism link: https://drive.google.com/open?id=0Bz4XFYwHwUBVRS1YOWN3T05KWms

Basic operations on DSP processor

This was demo experiment on how to use DSP processor for real life applications. In this we use C2000 DSP kit for performing various basic operations like addition, subtraction, multiplication and some logical operations.

FIR filter design using Frequency sampling

In this experiment, the various parameters like pass band attenuation, stop band attenuation, pass band frequency, stop band frequency and sampling frequency are passed as input and the order of the filter is calculated. First Hd(w) is calculated and then H(k) is calculated by sampling and then after that h(n) by IDFT. The final output h(n) is always symmetric about the point of symmetry i.e. N/2. Discontinuity is observed in phase plot when the spectrum goes out of range that is from -pi to +pi.

FIR filter design using window function

In this experiment, we design a linear phase FIR filter - low pass and high pass. In this experiment, the desired impulse response is multiplied with window function w (n) to obtain h (n) which after Z-transform yields the transfer function H (z).

There are many types of window function i.e. Rectangular, Bartlett, Hamming, Hanning and Blackman. Attenuation in stop band is maximum for Blackman window and minimum for Rectangular window.

Various parameters like Ape, As, stop band frequency, pass band frequency and sampling frequency are taken as inputs from the user. It was observed that as order of the filter increases the no. of lobes in the frequency response increases.

Chebyshev Filter design

In this experiment we designed the chebyshev low pass and high pass filter using Scilab. In this experiment, various parameter like pass band attenuation, stop band attenuation, pass band frequency, stop band frequency and sampling frequency are passed as an input and the order of filter is calculated by plotting the magnitude spectrum.

In chebyshev filter there are ripples in pass band but monotonic in stop band. The no. of ripple peaks represent the order of filter. Order of chebyshev filter is always less than that of order of butterworth filter for same input parameters and therefore chebyshev filter requires less hardware components for realisation.

Butterworth Filter Design

This was the experiment of learning and designing Butterworth low pass and high pass filter using Scilab. After understanding Scilab we came to know that there are various functions such as buttmag() for calculating transfer function and order of the filter. Also there are various functions for calculating fft, magnitude response in Scilab.

From this experiment we understood that butterworth filter have flat response and a steep transmission band depending on the order of filter. We designed both analog filter and digital filter and we can see from both the filters whether the filter is stable or not. If analog poles lie on LHS of s-plane so analog is filter is stable. If digital poles lie inside unit circle so didital filter is stable. Also if the magnitude spectrum is monotonic in both pass band as well as stob band i.e. no ripples.