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

"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...

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Bibliographic Details
Main Author:	Lerch, Alexander
Format:	eBook
Language:	English
Published:	Hoboken, N.J. : Wiley, [2012]
Series:	Wiley Online Library.
Subjects:	Computational auditory scene analysis. Computer sound processing. Content analysis (Communication) > Data processing. COMPUTERS / Optical Data Processing. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

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.
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