IEEE Paper review

After performing all concept of dspp now we study on application of dspp. For that we select one application IEEE paper and patent. This all procedure is tough and its content is also difficult to quick understand.

In this blog i am going to give review about IEEE paper. My dspp application is audio filter.

IEEE paper:-

 Accurate Discretization of Analog Audio Filters With Application to Parametric Equalizer Design

This IEEE paper is published bySimo Särkkä, Member, IEEE, and Antti Huovilainen

When the formal continuous time system is sampled, the resulting filter reduces to discrete linear filter, which can be realized either as a state space model or as an infinite impulse response (IIR) filter. The proposed methodology is applied to design of filters for parametric equalizers.
Results: This is implemented on texas DSP board TMS320C6201. The code is written in C language and implemented in code composer studio and matlab. The analyzed speed, memory, channel separetion.
The proposed method is based on quite elementary ideas, namely to approximation of the Shannon interpolation and numerical solutions of the corresponding LTI differential equations. Thus, it is possible that similar methodology has been already used. They have presented a general discretization method, which can be used for designing discrete versions of analog filters such that the frequency response of the digital design accurately matches that of the analog design.

Patent review

Previous blog i gave review on IEEE paper on topic FM demodulation. now m going to talk about Patent on it. It's littel difficult to understand but whatever i understood that i going to write.

Patent:-

 DYNAMIC DIGITAL IIR AUDIO FILTER AND METHOD WHICH PROVIDES DYNAMIC DIGITAL FILTERING FOR AUDIO SIGNALS  

This patent is publish on United state (Patent Application Publication Sundaresan)

Pub. N0.: USOO5170369A
Patent no.: 5,170,369

Pub. Date: Dec. 8, 1992

Inventor: David P. Rossum, Amos’ Calif.

(Us)

The present invention relates to a dynamic digital IIR audio ?lter and corresponding method. The current state of the art for digital in?nite impulse response (IIR) ?lters for audio applications utilizes long wordlengths to obtain adequate noise performance for adequate sound quality. advantages and novel features of the present invention will be set forth in part in the description which follows and in part become apparent to those skilled in the art upon examination of the fol lowing or ay be learned by practice of the invention.  

analog and digital butterworth filter design

In the fifth experiment we perform analog and digital butterworth filter design. In the experiment we also study the aliasing effect due to sampling in IIM method and frequency warping effect in BLT method.

IIM: Mapping is many to one. No frequency warping effect is present. It is not suitable to design HPF and BRF.

BLT: Mapping is one to one. Frequency warping effect is present. HPF and BRF can be designed. 

Because of non-linear mapping; the amplitude  response of digital IIR filter is expanded at lower frequencies and compressed at higher frequencies in comparison to the analog filter is called frequency warping.

Analog and digital chebyshev filter design

In the sixth experiment we perform analog and digital chebyshev filter design.
There are two groups in this filter:

1. Type-1 chebyshev filter:
These filter are all pole filters. In the passband, they have monotonic characteristics in the stopband.
2. Type-2 chebyshev filter:
This filter contains zeros as well as poles.

The major difference between butterworth and chebyshev filter is that the poles of butterworth filter lie on the circle, whilethe poles of chebyshev filter lie on ellipse. 

FIR filter design using window method

In the seventh experiment we perform FIR filter design using window method.
Windowing method requires minimum amount of computational effort; so this method is simple to implement.
properties of commonly used windows:
1. Hamming method
2. Hanning method
3. Triangular method
4. Blackman method
5. Rectangular method
6. Kaiser method
When in numerical window function is not given then, by observing frequency domain characteristics we have to select a proper window function. 

FIR filter design using frequency sampling method

In the eighth experiment we perform FIR design using frequency sampling method.
Here we design digital filter in scilab.
The output h[n] is obtained by performing inverse-DFT.
As discrete values of the frequency response. it can be considered that the response is sampled, hence the name FSM.
Required data given was cutoff r=frequency and order of the filter.

Perform operation using DSP processor

Our ninth experiment is basic operations of DSP Processor.
In this experiment we learn programming using DSP hardware.
We perform addition, Subtraction, Multiplication and Division using processor.
Also perform logic and shifting operations.
Its quite difficult to perform or understand but i done this by taking my classmets help.

Filtering of data sequence using OSM & OAM

In the fourth experiment we perform OAM and OSM. Fast convolution can be accomplished by overlap add(OA) or Overlap save(OS) methods.
Two methods are used to evaluate the discrete convolution −

Overlap-save method : If the components are observed, the
values from both convolutions result in the desired “fully engaged” filter during this region in
time. Therefore, overlapping smaller length convolutions and then summing the appropriate
overlapping segments will form the continuous output. This approach is called the “OverlapAdd
Method” for continuous signal processing.

Overlap-add method : Overlap–save is the traditional name for an efficient way to evaluate the discrete convolution
between a very long signal x(n) and a finite impulse response (FIR) filter h(n).

Fast Fourier Transform

In the third experiment we pwerform FFT. The Fast Fourier Transform (FFT) is one of the most important in signal processing and data analysis, but the FFT is a complicated algorithm.
In complex notation, the time and frequency domains each contain one signal made up of N complex points. Each of these complex points is composed of two numbers, the real part and the imaginary part.
The FFT operates by decomposing an N point time domain signal into N time domain signals each composed of a single point. The second step is to calculate the N frequency spectra corresponding to these N time domain signals. Lastly, the N spectra are synthesized into a single frequency spectrum.

Discrete Fourier Transform spectrum

In the second experiment we study the magnitude spectrum of DFT signal. The discrete Fourier transform takes in data and gives out the frequencies that the data contains.
This is useful if you want to analyze data, but can also be useful if you want to modify the frequencies then use the inverse discrete Fourier transform to generate the frequency modified data.
The shape of the time domain waveform is not important in these signals; the key information is in the frequency, phase and amplitude of the component sinusoidal.
The DFT is used to extract this information.

Linear convolution and circular convolution

In the first experiment we performed the Linear convolution and circular convolution.

Linear convolution: Linear convolution shifts linearly. It is operation to calculate the output for any linear time invariant system given its input and its impulse response. we can perform linear convolution from circular convolution, but the thing zero padding must be done upto L+M-1 inputs.

Circular convolution: Circular convolution shifts circularly. It is the same as linear convolution but considering that the support of the signal is periodic. Convolution is done but in circular pattern, depending upon the samples of the signal.