Patent And Paper Review

Patent Review

Patent No: US 3668542

Publication date: June 6,1972                                                                                                   Inventors: Daniel W. Stoffer C.      

Title : Audio Compression Circuit Using OpAmp and JFET

Summary :

 Audio Compression reduces the dynamic range of the any recording by bringing down the level of the loudest parts, meaning the loud and quiet parts are now closer together in volume and the natural volume variations are less obvious. This generally helps in mixing of musical tracks without a significant fight. The audio compression circuit mentioned in this patent promises a good frequency response and fast attack. The mentioned technique makes use of a p-channel JFET connected to an Op-amp input. The FET acts as a voltage controlled amplifier in the Operational amplifier feedback loop. This invention thereby aims to provide gain control not affecting the dc bias and also free of settling time limitations which otherwise  poses to be a major problem in most audio compression circuits.

Patent Link : https://www.google.com/patents/US3668542

Paper Review

Journal : International Journal of Advanced Research in Computer and Communication Engineering

Title : Analysis and Comparison of Audio Compression Using Discrete Wavelet Transform

Volume 86 – No 13, January 2014                                         

Summary :

Speech compression is a technique of converting human speech into  an efficiently encoded signal which can later be decoded to produce the approximate original back again. Compression not only reduces storage space but also bandwidth transmission power and energy. The implemented technique Discrete wavelet Transform (DWT) decomposes original signal into wavelet coefficients at different scales and positions which are later truncated to perform encoding or decoding. It was also found that this technique works best for recorded signals compared to the real time ones.

Paper Link : https://www.researchgate.net/publication/272384288_Analysis_and_Comparison_of_Audio_Compression_Using_Discrete_Wavelet_Transform

Plagiarism Check Link :  https://drive.google.com/drive/folders/0BxzVWYbaSXFrMUtVRzNZWXpLeTQ

Basic Operations on DSP Processor

Basic mathematical operations were implemented on the DSP processor kit and the results were verified in this experiment. We used a custom board of popular C2000 processor and the code composer studio platform was used for the coding purpose.A new project was created and a simple code was written to demonstrate various arithmetic and logical instructions on the processor. The output of the instructions was stored in the registers and these values were obtained in real-time using the debugging functionality of the software.

FIR Filter design using Frequency Sampling Method (FSM)

A filter is basically a system which allows a particular band of frequencies to pass and selectively changes the wave shape, phase- frequency characteristics in a desired fashion.In this method the given frequency response is sampled as a set of equally sampled frequencies to obtain N samples.The continuous frequency response Hd(w) at N points essentially gives us the N-Point DFT of Hd(2pnk/N).Thus by using IDFT formula, the filter co-efficients can be calculated. Increasing the number of samples can possibly reduce the error. Thus FIR design was implemented using FSM method.

FIR Filter Design using Windowing method

In this experiment we designed a FIR filter with the help of windowing method. A window function is basically used to truncate the infinite values obtained after the  IDTFT operation on the phase response function. A suitable window function is selected depending upon the stop band attenuation which is an input specification.. The output response was observed in the form of a graph on the scilab software and results was verified. 

Chebyshev Filter Design

Here we designed a digital chebyshev filter using the analog filter and input specifications. Scilab software was used to write the code and also observe the output magnitude spectrum with frequency (Hz) on X axis and attenuation (dB) on Y axis. The conspicuous feature of the magnitude spectrum was that it exhibited ripple in passband and no monotonic in stop band. Moreover for almost similar input parameters the order of chebyshev filter was lesser compared to butterworth filter. That meant the chebyshev filter required less hardware components for its realization

Butterworth Filter Design

The task was to design a digital Butterworth filter from an analog filter and with the help of input specifications. Scilab software was used to code and observe the output plot with frequency on x axis and attenuation (in db) on Y axis.2 cases were studied, one with the low pass filter and other with the high pass design.The pass band and stop band attenuation values which were fed in the code were later verified with the help of graph for the corresponding analog pass band and stop band frequencies respectively.From the pole zero plot observed in scilab we saw that in case of digital LPF ,poles lay inside the unit circle, thus the digital filter was stable in nature.

Overlap Add and Overlap save Method

In this session we studied and practically implemented the commonly used block processing techniques namely OAM(Overlap Add method) and OSM(Overlap save method).The general idea here is that when the input to a digital FIR filter is a very long sequence, performing convolution of the entire signal could prove to be an arduous task.Thus a very efficient way of finding the filter output is by using the linear filtering methods viz. OAM ,OSM wherein we decompose the original input signal and perform linear convolution on each of them individually. OAM and OSM are thus most suitable for processing the real time signals.

Fast Fourier Transform

As the name suggested FFT went on to prove itself to be computationally faster than its discrete counterpart. This was the very result we verified in our third lab session.Two cases were considered, , one with a four point input and other with an eight point input.The number of real additions and multiplications (Computations) were considerably lesser compared to the DFT computations.This very well justified the computational efficiency of the Fast Fourier Transform.   

Discrete Fourier Transform

Initially a four point and later an eight point signal was fed as the input to implement the Discrete Fourier Transform which basically converted any signal from time to frequency domain.Thus in simple words DFT was nothing but the frequency sampled version of DTFT(Discrete time fourier transform).The results produced in both cases were found to be periodic in nature.Magnitude spectrums were plotted for each case which highlighted the effect of increasing the input signal length on frequency spacing , approximation error and spectrum resolution.The most revealing observation however was that as we expanded the input in time domain we achieved a compressed spectra in frequency domain.

Convolution and Correlation algorithms

The basic mathematical operations of convolution (Linear, Circular) and correlation(Auto, Cross) were studied and practically implemented in this session.It was observed that linear convolution produced a causal output for a causal input. Circular convolution however produced an aliased output.That means first few values of the output got overlapped with the values beyond 'N' where N acted as the length of input and output signals.Correlation was used to test the degree of similarity between two signals.It was observed that auto correlation signal always turned out to be an even signal.C programming language was used to construct the test codes.