Low Bit Rate Speech Coding Using TMS320C6416

The title of the project is Low Bit Rate Speech Coding Using TMS320C6416 DSP Processor. The scope of this project is divided into two main parts. Part one involves the study of the TMS320C6416 DSP processor. My task was to understand the architecture of this board and complete the tutorials in Code Composer Studio (CCS). The second part is concerned with the sampling of speech signal (analog signal) at different sampling frequencies and to study its effects on the quality of the reconstructed speech signal. Initially MATLAB and SIMULINK were used to sample the speech file and to study the effect of variation in sampling frequency on the quality of the speech signal and its waveform. Later, the sampling process is implemented in real time using the TMS320C6416 DSP Processor. Three sampling frequencies were chosen which are 8000 Hz, 4000 Hz and 2000 Hz. The results were divided into two sections; before real-time implementation and after real-time implementation. The comparison of the quality of the sampled audio signal was carried out for the three sampling frequencies as mentioned earlier. Two methods were used to measure the quality of the reconstructed audio signal. First, fifteen students were chosen to rate their score for the quality of the reconstructed signal. The score range was from 1(bad) to 5(excellent). Secondly, scope was used to display the waveform of the original and reconstructed signal. The results showed that the quality of the sound degrades from 8000 Hz to 2000 Hz

An Introduction to Digital Signal Processing

An Introduction to Digital Signal Processing aims at undergraduate students who have basic knowledge in C programming, Circuit Theory, Systems and Simulations, and Spectral Analysis. The book is focused on basic concepts of digital signal processing, MATLAB simulation and implementation on selected DSP hardware in which the candidate is introduced to the basic concepts first before embarking to the practical part which comes in the later chapters. Initially Digital Signal Processing evolved as a postgraduate course which slowly filtered into the undergraduate curriculum as a simplified version of the latter. The goal was to study DSP concepts and to provide a foundation for further research where new and more efficient concepts and algorithms can be developed. Though this was very useful it did not arm the student with all the necessary tools that many industries using DSP technology would require to develop applications. This book is an attempt to bridge the gap. It is focused on basic concepts of digital signal processing, MATLAB simulation and implementation on selected DSP hardware. The objective is to win the student to use a variety of development tools to develop applications. Contents• Introduction to Digital Signal processing.• The transform domain analysis: the Discrete-Time Fourier Transform• The transform domain analysis: the Discrete Fourier Transform• The transform domain analysis: the z-transform• Review of Analogue Filter• Digital filter design.• Digital Signal Processing Implementation Issues• Digital Signal Processing Hardware and Software• Examples of DSK Filter Implementatio

An Introduction to Digital Signal Processing

An Introduction to Digital Signal Processing aims at undergraduate students who have basic knowledge in C programming, Circuit Theory, Systems and Simulations, and Spectral Analysis. The book is focused on basic concepts of digital signal processing, MATLAB simulation and implementation on selected DSP hardware in which the candidate is introduced to the basic concepts first before embarking to the practical part which comes in the later chapters. Initially Digital Signal Processing evolved as a postgraduate course which slowly filtered into the undergraduate curriculum as a simplified version of the latter. The goal was to study DSP concepts and to provide a foundation for further research where new and more efficient concepts and algorithms can be developed. Though this was very useful it did not arm the student with all the necessary tools that many industries using DSP technology would require to develop applications. This book is an attempt to bridge the gap. It is focused on basic concepts of digital signal processing, MATLAB simulation and implementation on selected DSP hardware. The objective is to win the student to use a variety of development tools to develop applications. Contents• Introduction to Digital Signal processing.• The transform domain analysis: the Discrete-Time Fourier Transform• The transform domain analysis: the Discrete Fourier Transform• The transform domain analysis: the z-transform• Review of Analogue Filter• Digital filter design.• Digital Signal Processing Implementation Issues• Digital Signal Processing Hardware and Software• Examples of DSK Filter Implementatio

DSP IMPLEMENTATION OF A DIGITAL NON-LINEAR INTERVAL CONTROL ALGORITHM FOR A QUASI-KEYHOLE PLASMA ARC WELDING PROCESS

The Quasi-Keyhole plasma arc welding (PAW) process is a relatively simple concept, which provides a basis for controlling the weld quality of a subject work piece by cycling the arc current between a static base and variable peak level. Since the weld quality is directly related to the degree of penetration and amount of heat that is generated and maintained in the system, the Non-Linear Interval Control Algorithm provides a methodology for maintaining these parameters within acceptable limits by controlling the arc current based upon measured peak current times. The Texas Instruments TMS320VC5416 DSK working in conjunction with Signalwares AED-109 Data Converter provides a hardware solution to implement this control algorithm. This study outlines this configuration process and demonstrates its validity

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes