An introduction to audio content analysis : applications in signal processing and music informatics /

• Κύριος συγγραφέας: Lerch, Alexander
• Μορφή: Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Hoboken, N.J. : Wiley, [2012]
• Θέματα: Computational auditory scene analysis.; Computer sound processing.; Content analysis (Communication) > Data processing.; COMPUTERS > Optical Data Processing.; Electronic books.

"With the proliferation of digital audio distribution over digital media, audio content analysis is fast becoming a requirement for designers of intelligent signal-adaptive audio processing systems. Written by a well-known expert in the field, this book provides quick access to different analys...

Πίνακας περιεχομένων:

• Machine generated contents note: 1.1. Audio Content
• 1.2.A Generalized Audio Content Analysis System
• 2.1. Audio Signals
• 2.1.1. Periodic Signals
• 2.1.2. Random Signals
• 2.1.3. Sampling and Quantization
• 2.1.4. Statistical Signal Description
• 2.2. Signal Processing
• 2.2.1. Convolution
• 2.2.2. Block-Based Processing
• 2.2.3. Fourier Transform
• 2.2.4. Constant Q Transform
• 2.2.5. Auditory Filterbanks
• 2.2.6. Correlation Function
• 2.2.7. Linear Prediction
• 3.1. Audio Pre-Processing
• 3.1.1. Down-Mixing
• 3.1.2. DC Removal
• 3.1.3. Normalization
• 3.1.4. Down-Sampling
• 3.1.5. Other Pre-Processing Options
• 3.2. Statistical Properties
• 3.2.1. Arithmetic Mean
• 3.2.2. Geometric Mean
• 3.2.3. Harmonic Mean
• 3.2.4. Generalized Mean
• 3.2.5. Centroid
• 3.2.6. Variance and Standard Deviation
• 3.2.7. Skewness
• 3.2.8. Kurtosis
• 3.2.9. Generalized Central Moments
• 3.2.10. Quantiles and Quantile Ranges
• 3.3. Spectral Shape
• 3.3.1. Spectral Rolloff.
• Contents note continued: 3.3.2. Spectral Flux
• 3.3.3. Spectral Centroid
• 3.3.4. Spectral Spread
• 3.3.5. Spectral Decrease
• 3.3.6. Spectral Slope
• 3.3.7. Mel Frequency Cepstral Coefficients
• 3.4. Signal Properties
• 3.4.1. Tonalness
• 3.4.2. Autocorrelation Coefficients
• 3.4.3. Zero Crossing Rate
• 3.5. Feature Post-Processing
• 3.5.1. Derived Features
• 3.5.2. Normalization and Mapping
• 3.5.3. Subfeatures
• 3.5.4. Feature Dimensionality Reduction
• 4.1. Human Perception of Intensity and Loudness
• 4.2. Representation of Dynamics in Music
• 4.3. Features
• 4.3.1. Root Mean Square
• 4.4. Peak Envelope
• 4.5. Psycho-Acoustic Loudness Features
• 4.5.1. EBU R128
• 5.1. Human Perception of Pitch
• 5.1.1. Pitch Scales
• 5.1.2. Chroma Perception
• 5.2. Representation of Pitch in Music
• 5.2.1. Pitch Classes and Names
• 5.2.2. Intervals
• 5.2.3. Root Note, Mode, and Key
• 5.2.4. Chords and Harmony
• 5.2.5. The Frequency of Musical Pitch
• 5.3. Fundamental Frequency Detection.
• Contents note continued: 5.3.1. Detection Accuracy
• 5.3.2. Pre-Processing
• 5.3.3. Monophonic Input Signals
• 5.3.4. Polyphonic Input Signals
• 5.4. Tuning Frequency Estimation
• 5.5. Key Detection
• 5.5.1. Pitch Chroma
• 5.5.2. Key Recognition
• 5.6. Chord Recognition
• 6.1. Human Perception of Temporal Events
• 6.1.1. Onsets
• 6.1.2. Tempo and Meter
• 6.1.3. Rhythm
• 6.1.4. Timing
• 6.2. Representation of Temporal Events in Music
• 6.2.1. Tempo and Time Signature
• 6.2.2. Note Value
• 6.3. Onset Detection
• 6.3.1. Novelty Function
• 6.3.2. Peak Picking
• 6.3.3. Evaluation
• 6.4. Beat Histogram
• 6.4.1. Beat Histogram Features
• 6.5. Detection of Tempo and Beat Phase
• 6.6. Detection of Meter and Downbeat
• 7.1. Dynamic Time Warping
• 7.1.1. Example
• 7.1.2.Common Variants
• 7.1.3. Optimizations
• 7.2. Audio-to-Audio Alignment
• 7.2.1. Ground Truth Data for Evaluation
• 7.3. Audio-to-Score Alignment
• 7.3.1. Real-Time Systems M
• 7.3.2. Non-Real-Time Systems.
• Contents note continued: 8.1. Musical Genre Classification
• 8.1.1. Musical Genre
• 8.1.2. Feature Extraction
• 8.1.3. Classification
• 8.2. Related Research Fields
• 8.2.1. Music Similarity Detection
• 8.2.2. Mood Classification
• 8.2.3. Instrument Recognition
• 9.1. Fingerprint Extraction
• 9.2. Fingerprint Matching
• 9.3. Fingerprinting System: Example
• 10.1. Musical Communication
• 10.1.1. Score
• 10.1.2. Music Performance
• 10.1.3. Production
• 10.1.4. Recipient
• 10.2. Music Performance Analysis
• 10.2.1. Analysis Data
• 10.2.2. Research Results
• A.1. Identity
• A.2.Commutativity
• A.3. Associativity
• A.4. Distributivity
• A.5. Circularity
• B.1. Properties of the Fourier Transformation
• B.1.1. Inverse Fourier Transform
• B.1.2. Superposition
• B.1.3. Convolution and Multiplication
• B.1.4. Parseval's Theorem
• B.1.5. Time and Frequency Shift
• B.1.6. Symmetry
• B.1.7. Time and Frequency Scaling
• B.1.8. Derivatives
• B.2. Spectrum of Example Time Domain Signals.
• Contents note continued: B.2.1. Delta Function
• B.2.2. Constant
• B.2.3. Cosine
• B.2.4. Rectangular Window
• B.2.5. Delta Pulse
• B.3. Transformation of Sampled Time Signals
• B.4. Short Time Fourier Transform of Continuous Signals
• B.4.1. Window Functions
• B.5. Discrete Fourier Transform
• B.5.1. Window Functions
• B.5.2. Fast Fourier Transform
• C.1.Computation of the Transformation Matrix
• C.2. Interpretation of the Transformation Matrix
• D.1. Software Frameworks and Applications
• D.1.1. Marsyas
• D.1.2. CLAM
• D.1.3.jMIR
• D.1.4.CoMIRVA
• D.1.5. Sonic Visualiser
• D.2. Software Libraries and Toolboxes
• D.2.1. Feature Extraction
• D.2.2. Plugin Interfaces
• D.2.3. Other Software.