The problem of acoustic noise is becoming increasingly serious with the growing use of industrial and medical equipment, appliances, and consumer electronics. Active noise control (ANC), based on the principle of superposition, was developed in the early 20th century to help reduce noise. However, ANC is still not widely used owing to the effectiveness of control algorithms, and to the physical and economical constraints of practical applications. In this paper, we briefly introduce some fundamental ANC algorithms and theoretical analyses, and focus on recent advances on signal processing algorithms, implementation techniques, challenges for innovative applications, and open issues for further research and development of ANC systems.

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Recent Advances on Active Noise Control: Open Issues and Innovative Applications

Master the basic concepts and methodologies of digital signal processing with this systematic introduction, without the need for an extensive mathematical background The authors lead the reader through the fundamental mathematical principles underlying the operation of key signal processing techniques, providing simple arguments and cases rather than detailed general proofs Coverage of practical implementation, discussion of the limitations of particular methods and plentiful MATLAB illustrations allow readers to better connect theory and practice A focus on algorithms that are of theoretical importance or useful in real-world applications ensures that students cover material relevant to engineering practice, and equips students and practitioners alike with the basic principles necessary to apply DSP techniques to a variety of applications Chapters include worked examples, problems and computer experiments, helping students to absorb the material they have just read Lecture slides for all figures and solutions to the numerous problems are available to instructors

Applied Digital Signal Processing: Theory and Practice

The root mean square mathematical operation is widely used in power engineering. The process has a frequency response characteristic and an associated time constant which is important especially for short term signals. Potential problem areas in using RMS values in power quality assessment are identified and discussed.

On the use of RMS values in power quality assessment

A new robust training algorithm for a class of single-hidden layer feedforward neural networks

In this paper, we extend the work by Ogihara, et al. to discriminate between human and synthetic speech using features based on pitch patterns. As previously demonstrated, significant differences in pitch patterns between human and synthetic speech can be leveraged to classify speech as being human or synthetic in origin. We propose using mean pitch stability, mean pitch stability range, and jitter as features extracted after image analysis of pitch patterns. We have observed that for synthetic speech, these features lie in a small and distinct space as compared to human speech and have modeled them with a multivariate Gaussian distribution. Our classifier is trained using synthetic speech collected from the 2008 and 2011 Blizzard Challenge along with Festival pre-built voices and human speech from the NIST2002 corpus. We evaluate the classifier on a much larger corpus than previously studied using human speech from the Switchboard corpus, synthetic speech from the Resource Management corpus, and synthetic speech generated from Festival trained on the Wall Street Journal corpus. Results show 98% accuracy in correctly classifying human speech and 96% accuracy in correctly classifying synthetic speech. Index Terms: Speaker recognition, Speech synthesis, Security

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Synthetic Speech Discrimination using Pitch Pattern Statistics Derived from Image Analysis

Preface. Chapter 1. Introduction to Real-Time Digital Signal Processing. Chapter 2. Introduction to TMS320C55x Digital Signal Processor. Chapter 3. DSP Fundamentals and Implementation Considerations. Chapter 4. Design and Implementation of FIR Filters. Chapter 5. Design and Implementation of IIR Filters. Chapter 6. Frequency Analysis and Fast Fourier Transform. Chapter 7. Adaptive Filtering. Chapter 8. Digital Signal Generators. Chapter 9. Dual-Tone Multi-Frequency Detection. Chapter 10. Adaptive Echo Cancellation. Chapter 11. Speech Coding Techniques. Chapter 12. Speech Enhancement Techniques. Chapter 13. Audio Signal Processing. Chapter 14. Channel Coding Techniques. Chapter 15. Introduction to Digital Image Processing. Appendix A: Some Useful Formulas and Definitions. A.1 Trigonometric Identities. A.2 Geometric Series. A.3 Complex Variables. A.4 Units of Power. References. Appendix B: Software Organization and List of Experiments. Index.

Wenshun Tian

Papers

Real-time digital signal processing

Introduction to Real‐Time Digital Signal Processing

Adaptive Echo Cancelation

Design and Implementation of FIR Filters

Speech Enhancement Techniques