Tuesday, 26 April 2016

DSPP Experiment No. 10 - Signal Processing Application

A DSP processor is a specialized microprocessor with its architecture optimized for the needs of Digital signal processing and our aim for this experiment is to undergo through a Signal processing application.The goal of this group Experiment of fetching out papers and patents on DSPP applications reviewing it and compiling a IEEE formatted report has been achieved and following order flows the report reviewed by Amey C. Thombre and Saumil S. Shah followed by the paper and patent reviewed.
Your Valuable comments and suggestions are much appreciated. 
Abstract - this work describes a process of a speech recognition system as an application of DSP processor designed and developed proportional and linearly to overcome the holistic technique of voice synthesis and human to computer interaction with a better, accurate and much appreciated methodology. The design and implementation of this process is done using MATLAB at the initial simulation level, a multi-paradigm numerical computing environment and fourth-generation programming language software. The project involves deep insights of data processing, modulation techniques, mathematical analysis and conversion and generation. The DSP Processor plays the play-making role in this construction as it is crucially held responsible for the implementation part. Generally, the input voice is recognized by the system for any type of input. But here, the apparatus is able to differentiate between various voice signals as well. This can be used for various security purposes because it can be trained to recognize a specific set of phrases by the user. Speech recognition is a very computationally intensive task and includes many of digital signal processing algorithms to be executed simultaneously which makes it hard and also beneficial as it increases the speed by parallel processing. Many Kits like HM2007 use this technique for voice recognition Harmonics may be present in the sinusoidal output due to non-linear loads. High harmonic content can cause excessive heating in the circuitry and give incorrect readings. It is therefore essential that the sinusoidal output generated has minimum harmonic content. 
IEEE Paper Review: DSP Based System for Real time Voice Synthesis Applications Development
Publisher : 
Radu Arsinte,Attila Ferencz
-Software ITC S.A.- 109 Gh.Bilascu Street -3400 Cluj-Napoca - Romania Phone:+40-64-197681,197682 Fax:+40-64-196787 Email:sitc@utcluj.ro Email:dianaz@utcluj.ro
Costin Miron Technical University Cluj-Napoca - Faculty of Electronics and Telecommunications 26-28 Gh.Baritiu Street ,3400 Cluj-Napoca,Romania
Summary : This paper describes an experimental system designed for development of real time voice synthesis applications. The system is composed from a DSP coprocessor card , equipped with an TMS320C25 or TMS320C50 chip, voice acquisition module (ADDA2) ,host computer (IBM-PC compatible), software specific tools.
The development system was used in the real time implementation of speech synthesiser based on the linear prediction method. Using DSP technology allows real-time synthesis of voice ,with high quality features. Compared with PC only based systems (without DSP) performances are higher then in a PC486DX4 or Pentium implementation, where is difficult to obtain real-time running with this method(as experiments revealed).On other side in PC throughput limitations occurs, extension bus being considerably slower than the CPU. After the development phase ,done on this system, we can design a dedicated system for consumer applications based on DSP ,the resulting system cost being significantly reduced.

Patent Review :
Adaptation of a speech recognition system across multiple remote sessions with a speaker.Patent No : US 6766295 B1
Filing Date : 20 JUlY 2004
Inventors : Hy MurveitAshvin KannanOrignal Assignee : Nuance Commnications
Summary : 
A technique for adaptation of a speech recognizing system across multiple remote communication sessions with a speaker. The speaker can be a telephone caller. An acoustic model is utilized for recognizing the speaker's speech. Upon initiation of a first remote session with the speaker, the acoustic model is speaker-independent. During the first session, the speaker is uniquely identified and speech samples are obtained from the speaker. In the preferred embodiment, the samples are obtained without requiring the speaker to engage in a training session. The acoustic model is then modified based upon the samples thereby forming a modified model. The model can be modified during the session or after the session is terminated. Upon termination of the session, the modified model is then stored in association with an identification of the speaker. During a subsequent remote session, the speaker is identified and, then, the modified acoustic model is utilized to recognize the speaker's speech. Additional speech samples are obtained during the subsequent session and, then, utilized to further modify the acoustic model. In this manner, an acoustic model utilized for recognizing the speech of a particular speaker is cumulatively modified according to speech samples obtained during multiple sessions with the speaker. As a result, the accuracy of the speech recognizing system improves for the speaker even when the speaker only engages in relatively short remote sessions.



DSPP Experiment No. 9 - FIR filter Design using frequency sampling method.

The aim which has to be achieved and the contents of this experiments are as follows : 
To design digital filter using frequency sampling method
We have to accept two inputs, one is for LPF and another is for HPF. After which we design a linear phaase as well as non linear FIR filter and lately plot a magnitude spectrum and phase spectrum and on verifying Ap and As in spectrum we jump to conclusions. Also we need to note that ripples in the stop band are obtained of decreasing amplitude. The phase plot is linear and similar to both Lpf and hpf if order of two filters is same since phase is linear the output will not be distorted. 

https://drive.google.com/open?id=0BxKOmgoubcmEbl9VSnJhX21WZ3M

DSPP Experiment No. 8 - Linear phase FIR filter using window function

The objective of this experiment:
1) Accept the Input for 2 cases.
2) Design the filter
3) Plot the magnitude spectrum4) verify values of Ap and As in pass band and stop band from the magnitude spectrum.

Thus it was verified that the phase spectrum of HPF anf BPF in FIR filter is LINEAR ! 


DSPP Experiment No. 7 - To perform operations using DSP processor

Start of use and understanding of DSP Processor

The dsp kit used TMS320f28375.
Study of Arithmetic (ADD, SUBTRACT, MULTIPLICATION),
logical (NOT AND )
and shifting operations(SHIFT RIGHT, SHIFT LEFT, ROTATE LEFT, ROTATE RIGHT).
The instructions for above operations were implemented and register values before and after execution were observed and noted down.

DSPP Experiment No. 6 - Analog and digital Chebyshev filter


Another work on Scilab. 
The aim which has to be achieved and the contents of this experiments are as follows :
Design a digital chebyshev filter. Inputs: pass band attenuation, stop band attenuation, pass band, stop band and sampling frequencies in Hz.
Two cases of I/P : one for HPF and LPF
use of BLT plot method,
 it was concluded that after increasing the order of the filter, we get better matching between input and output values.

https://drive.google.com/open?id=0BxKOmgoubcmEWEpZcFljeDNQRzg

DSPP Experiment No. 5 - Analog and digital Butterworth filter

Inception of the work on Scilab. 
The aim which has to be achieved and the contents of this experiments are as follows :
Design of Analog and digital Butterworth filterDesign of analog filter is done by calculating normalized and denormalized filter and then using BLT to find digital filter trnasfer function. magnitude response of Digital low pass filter and digital high pass filter was plotted. On a comparision of therotical values and observed values and observing th pole zero plots we concluded that observed values approach therotical values from response for LPF and HPF.


https://drive.google.com/open?id=0BxKOmgoubcmEcV84anhPZnNveGs

DSPP Experiment No. 4 - Filtering of Long Data Sequence using overlap add method and overlap save method.

The aim which has to be achieved and the contents of this experiments are as follows :
Filtering of Long Data Sequence using overlap add method and overlap save method. .
we took the length 
of the sequence as 8 
This method more importantly is suitable to find LC of long output sequence using FFT
This method finds its play role in Real time signal processing which eventually turns out to be one turn on factor as they reduce the delay in the output.
In OSM the decomposed signal of the first short length consists of zero, since the second signal consists the first signal as well, it saves the data.


https://drive.google.com/open?id=0BxKOmgoubcmEbDlFQ3czTmIwMEk

https://drive.google.com/open?id=0BxKOmgoubcmEd3JpNFVfMGlPbkk

DSPP Experiment No. 3 - Fast Fourier Transform

The aim which has to be achieved and the contents of this experiments are as follows :
Fast Fourier Transform 
we have undertook two cases : 
Case 1 : N=4
Case 2 : N=8
after which we obtsain complex multiplication, complex addition, real multiplication, real addition for analysis and comparision between DFT and FFT 
through which we conclude, Thus by using FFT we can reduce number of computation as compared to DFT. We can compare number of computations which help us in deciding the better method. 

link : https://drive.google.com/open?id=0BxKOmgoubcmEMmg2b3pJSmFBbzg

DSPP Experiment No. 2 - Discrete Fourier Transform

The aim which has to be achieved and the contents of this experiments are as follows :
 Discrete Fourier Transform and Inverse Discrete Fourier Transform
We have performed using C.
On analysis we saw, as number of values were increased in case II the quality of spectrum is IMPROVED.  One important factor is of the magnitude spectrum of the output which is obtained by Discrete Fourier Transform. Two cases with input signals are taken.
we concluded that 
- As N increases :
   - frequency spacing reduces
   - Approximation error in representation of spectrum decreases
   - Resolution Increases

Also one important factor noticed is that, The visual Apperance of Signal (Quality) INCREASES ! 

Monday, 25 April 2016

DSPP Experiment No. 1 - Correlation & Convolution

The aim which has to be achieved and the contents of this experiments are as follows :
1)Linear Convolution, Circular concolution
2) Auto Correlation
3) Cross Correlation
4) Linear Convolution using Circular Convolution
We have studied, understood, calculated, formulated and verified the above entities using mathematical formulation.There are various operations involved in each of the following like calculation of the outputs with use of legnth of input signals and other properties involved.  In convolution especially, when both the inputs are casual, output is also casual. In the later part aim revolved around studing mathematical operation of correlation and measure degree of similarity between two signals. We wrote a function to find correlation operation. We calculated correlation of a DT signals and verify the results . We also measured the degree of similarity.

https://drive.google.com/open?id=0BxKOmgoubcmEYml1UmMyUHVOekE

https://drive.google.com/open?id=0BxKOmgoubcmEM2pVU3JRZTVLOFk