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Produktbild: Audio Spoof Detection from Theory to Practical Application

Audio Spoof Detection from Theory to Practical Application

68,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

21.05.2026

Abbildungen

schwarz-weiss Illustrationen, Raster,schwarz-weiss, Zeichnungen, schwarz-weiss, Tabellen, schwarz-weiss

Verlag

Taylor and Francis

Seitenzahl

242

Maße (L/B/H)

15,4/23,5/1,8 cm

Gewicht

402 g

Sprache

Englisch

ISBN

978-1-03-291264-6

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

21.05.2026

Abbildungen

schwarz-weiss Illustrationen, Raster,schwarz-weiss, Zeichnungen, schwarz-weiss, Tabellen, schwarz-weiss

Verlag

Taylor and Francis

Seitenzahl

242

Maße (L/B/H)

15,4/23,5/1,8 cm

Gewicht

402 g

Sprache

Englisch

ISBN

978-1-03-291264-6

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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Die Leseprobe wird geladen.
  • Produktbild: Audio Spoof Detection from Theory to Practical Application

  • Author Biographies. Foreword. Preface. Chapter 1: Introduction. 1.1 Background. 1.2 Definition. 1.3 History. 1.4 Real and Fake Audio. 1.5 Emerging Threats in Voice-Based Fraud. 1.6 How AI Voice Scams are Taking Place. 1.7 Book Organization. Chapter 2: Audio Signal Processing. 2.1 Human Hearing. 2.2 Anatomy of the Auditory System. 2.3 How We Hear. 2.4 Psychoacoustics: The Science of Sound Perception. 2.5 What Are Filters?. 2.6 Hearing and Sound Waves. 2.7 Basic Qualities of Sound. 2.8 Digital Audios. 2.9 Audio Preprocessing Techniques. 2.10 Application of Audio Processing. 2.11 Attacks on ASV. 2.12 Conclusion. Chapter 3: Feature Extraction. 3.1 Introduction. 3.2 Fundamentals Used in Audio Signal Processing. 3.3 Taxonomy of Audio Features. 3.4 Perceptual Features. 3.5 Statistical and Temporal Features. 3.6 Challenges in Audio Feature Extraction. 3.7 Future Trends. 3.8 Conclusion. Chapter 4: Backend Classification. 4.1 Introduction. 4.2 Backend Classification Strategies for ASD. 4.3 Conclusion. Chapter 5: Attacks on ASV System. 5.1 Introduction. 5.2 History of Spoof Attack. 5.3 Fake Audio Generation. 5.4 Attacks on ASV. 5.5 Conclusion. Chapter 6: Data Augmentation. 6.1 Introduction. 6.2 Data Augmentation Techniques. 6.3 Applications of Data Augmentation in Speech Processing. 6.4 Conclusion. Chapter 7: Evaluation Metrics. 7.1 Introduction. 7.2 Overview of Evaluation Metrics. 7.3 Conclusion. Chapter 8: Datasets in Audio Spoof Detection. 8.1 Introduction. 8.2 Dataset Characteristics. 8.3 Datasets. 8.4 Dataset Generation Techniques. 8.5 Challenges in Audio Spoof Detection Dataset Design. 8.6 Future Directions for Dataset Development. 8.7 Conclusion. Chapter 9: Recent Trends and Open Issues. 9.1 Generalization and Application of the Proposed Work. 9.2 Suggestions for Future Work. Chapter 10: Implementation of the ASD System using Python. 10.1 Introduction. 10.2 System Requirements. 10.3 Dataset Handling. 10.4 Feature Extraction. 10.5 Machine Learning and Deep Learning Models for Audio Classification. Index.