Introduction to Audio Analysis : A MATLAB® Approach
Theodoros. Giannakopoulos
Bok Engelsk 2014 · Electronic books.
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| Utgitt | Burlington : : Elsevier Science, , 2014.
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| Omfang | 1 online resource (283 p.)
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| Opplysninger | Description based upon print version of record.. - Half Title; Title Page; Copyright; Contents; Preface; Acknowledgments; List of Tables; List of Figures; Part One: Basic Concepts, Representations and Feature Extraction; 1 Introduction; 1.1 The MATLAB Audio Analysis Library; 1.2 Outline of Chapters; 1.3 A Note on Exercises; 2 Getting Familiar with Audio Signals; 2.1 Sampling; 2.1.1 A Synthetic Sound; 2.2 Playback; 2.3 Mono and Stereo Audio Signals; 2.4 Reading and Writing Audio Files; 2.4.1 WAVE Files; 2.4.2 Manipulating Other Audio Formats; 2.5 Reading Audio Files in Blocks; 2.6 Recording Audio Data. - 2.6.1 Audio Recording Using the Data Acquisition Toolbox2.6.2 Audio Recording Using the Audio Recorder Function; 2.7 Short-term Audio Processing; 2.8 Exercises; 3 Signal Transforms and Filtering Essentials; 3.1 The Discrete Fourier Transform; 3.2 The Short-Time Fourier Transform; 3.3 Aliasing in More Detail; 3.4 The Discrete Cosine Transform; 3.5 The Discrete-Time Wavelet Transform; 3.6 Digital Filtering Essentials; 3.7 Digital Filters in MATLAB; 3.8 Exercises; 4 Audio Features; 4.1 Short-Term and Mid-Term Processing; 4.1.1 Short-Term Feature Extraction. - 4.1.2 Mid-Term Windowing in Audio Feature Extraction4.1.3 Extracting Features from an Audio File; 4.2 Class Definitions; 4.3 Time-Domain Audio Features; 4.3.1 Energy; 4.3.2 Zero-Crossing Rate; 4.3.3 Entropy of Energy; 4.4 Frequency-Domain Audio Features; 4.4.1 Spectral Centroid and Spread; 4.4.2 Spectral Entropy; 4.4.3 Spectral Flux; 4.4.4 Spectral Rolloff; 4.4.5 MFCCs; 4.4.6 Chroma Vector; 4.5 Periodicity Estimation and Harmonic Ratio; 4.6 Exercises; Part Two: Audio Content Characterization; 5 Audio Classification; 5.1 Classification Fundamentals; 5.1.1 The Bayesian Classifier. - 5.1.2 Classifier Training and Testing5.1.3 Multi-Class Problems; 5.2 Popular Classifiers; 5.2.1 The k-Nearest-Neighbor Classifier (k-NN); 5.2.2 The Perceptron Algorithm; 5.2.3 Decision Trees; 5.2.4 Support Vector Machines; 5.3 Implementation-Related Issues; 5.3.1 Training; 5.3.2 Testing; 5.4 Evaluation; 5.4.1 Performance Measures; 5.4.2 Validation Methods; 5.5 Case Studies; 5.5.1 Multi-Class Audio Segment Classification; 5.5.2 Speech vs Music Classification of Audio Segments; 5.5.3 Musical Genre Classification; 5.5.4 Speech vs Non-Speech Classification; 5.6 Exercises; 6 Audio Segmentation. - 6.1 Segmentation with Embedded Classification6.1.1 Fixed-Window Segmentation; 6.1.1.1 Naive Merging; 6.1.1.2 Probability Smoothing; 6.1.1.3 Example: Speech-Silence Segmentation; 6.1.1.4 Example: Fixed-Window Multi-Class Segmentation; 6.1.2 Joint Segmentation-Classification Based on Cost Function Optimization; 6.2 Segmentation Without Classification; 6.2.1 Signal Change Detection; 6.2.2 Segmentation with Clustering; 6.2.2.1 A Few Words on Data Clustering; 6.2.2.2 A Speaker Diarization Example; 6.3 Exercises; 7 Audio Alignment and Temporal Modeling; 7.1 Audio Sequence Alignment. - 7.1.1 Dynamic Time Warping. - Introduction to Audio Analysis serves as a standalone introduction to audio analysis, providing theoretical background to many state-of-the-art techniques. It covers the essential theory necessary to develop audio engineering applications, but also uses programming techniques, notably MATLAB®, to take a more applied approach to the topic. Basic theory and reproducible experiments are combined to demonstrate theoretical concepts from a practical point of view and provide a solid foundation in the field of audio analysis. Audio feature extraction, audio classification, audio se
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| ISBN | 9780080993881
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