TY  - BOOK
AU  - Lerch,Alexander
TI  - An introduction to audio content analysis: applications in signal processing and music informatics 
SN  - 9781118393550
AV  - TK7881.4 .L485 2012
U1  - 006.4/5 23
PY  - 2012///
CY  - Hoboken, N.J.
PB  - Wiley
KW  - Computational auditory scene analysis
KW  - Computer sound processing
KW  - Content analysis (Communication)
KW  - Data processing
KW  - COMPUTERS
KW  - Optical Data Processing
KW  - bisacsh
KW  - fast
KW  - Electronic books
N1  - Includes bibliographical references and index; 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; eee
N2  - "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 analysis algorithms and allows comparison between different approaches to the same task, making it useful for newcomers to audio signal processing and industry experts alike. A review of relevant fundamentals in audio signal processing, psychoacoustics, and music theory, as well as downloadable MATLAB files are also included"-
UR  - http://dx.doi.org/10.1002/9781118393550
ER  -