| 000 | 09818nam a2201081 i 4500 | ||
|---|---|---|---|
| 001 | 6266785 | ||
| 003 | IEEE | ||
| 005 | 20200421114417.0 | ||
| 006 | m o d | ||
| 007 | cr |n||||||||| | ||
| 008 | 151221s2012 nju ob 001 eng d | ||
| 020 |
_a9781118393550 _qebook |
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| 020 |
_z9781118266823 _qprint |
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| 020 |
_z1118393554 _qelectronic |
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| 020 |
_z9781118393505 _qelectronic |
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| 020 |
_z1118393503 _qelectronic |
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| 024 | 7 |
_a10.1002/9781118393550 _2doi |
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| 035 | _a(CaBNVSL)mat06266785 | ||
| 035 | _a(IDAMS)0b000064818b36cf | ||
| 040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
||
| 050 | 4 |
_aTK7881.4. _bL47 2012eb |
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| 082 | 0 | 0 |
_a006.4/5 _223 |
| 100 | 1 |
_aLerch, Alexander, _eauthor. |
|
| 245 | 1 | 3 |
_aAn introduction to audio content analysis : _bapplications in signal processing and music informatics / _cAlexander Lerch. |
| 264 | 1 |
_aHoboken, New Jersey : _bWiley, _cc2012. |
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| 264 | 2 |
_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2012] |
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| 300 | _a1 PDF (xxii, 248 pages). | ||
| 336 |
_atext _2rdacontent |
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| 337 |
_aelectronic _2isbdmedia |
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| 338 |
_aonline resource _2rdacarrier |
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| 504 | _aIncludes bibliographical references. | ||
| 505 | 8 | _aMachine 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 -- | |
| 505 | 8 | _aContents 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 -- | |
| 505 | 8 | _aContents 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 -- | |
| 505 | 8 | _aContents 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 -- | |
| 505 | 8 | _aContents 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. | |
| 506 | 1 | _aRestricted to subscribers or individual electronic text purchasers. | |
| 520 | _aAn easily accessible, hands-on approach to digital audio signal processingWith the proliferation of digital audio distribution over digital media, the amount of easily accessible music is ever-growing, requiring new tools for navigating, accessing, and retrieving music in meaningful ways. An understanding of audio content analysis is essential for the design of intelligent music information retrieval applications and content-adaptive audio processing systems.This book is about how to teach a computer to interpret music signals, thus allowing the design of tools for interacting with music. This book serves as a comprehensive guide on audio content analysis and how to apply it in signal processing and music informatics. Written by a well-known expert in the music industry, An Introduction to Audio Content Analysis ties together topics from audio signal processing and machine learning, showing how to use audio content analysis to pick up musical characteristics automatically. The author clearly explains the analysis of audio signals and the extraction of metadata describing the content of the signal, covering both abstract descriptions of technical properties and musical descriptions such as tempo, harmony and key, musical style, and performance attributes. Musical information is given a separate analysis in each category, whether tonal, pitch, harmony, key, temporal, or tempo, among others.Readers will get access to various analysis algorithms and learn to compare different standard approaches to the same task. The book includes a review of the fundamentals of audio signal processing, psychoacoustics, and music theory.An invaluable guide for newcomers to audio signal processing and industry experts alike, An Introduction to Audio Content Analysis also features downloadable MATLAB files from a companion website, www.AudioContentAnalysis.org, lists of abbreviations and symbols, and references. | ||
| 530 | _aAlso available in print. | ||
| 538 | _aMode of access: World Wide Web | ||
| 588 | _aDescription based on PDF viewed 12/21/2015. | ||
| 650 | 0 |
_aContent analysis (Communication) _xData processing. |
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| 650 | 0 | _aComputational auditory scene analysis. | |
| 650 | 0 | _aComputer sound processing. | |
| 655 | 0 | _aElectronic books. | |
| 695 | _aAccuracy | ||
| 695 | _aAlgorithm design and analysis | ||
| 695 | _aAnalytical models | ||
| 695 | _aApproximation methods | ||
| 695 | _aBandwidth | ||
| 695 | _aBooks | ||
| 695 | _aContext | ||
| 695 | _aData mining | ||
| 695 | _aDatabases | ||
| 695 | _aDegradation | ||
| 695 | _aDistortion | ||
| 695 | _aFeature extraction | ||
| 695 | _aFingerprint recognition | ||
| 695 | _aFrequency measurement | ||
| 695 | _aHarmonic analysis | ||
| 695 | _aHeuristic algorithms | ||
| 695 | _aHumans | ||
| 695 | _aIndexes | ||
| 695 | _aInstruments | ||
| 695 | _aInterpolation | ||
| 695 | _aLow pass filters | ||
| 695 | _aMicrophones | ||
| 695 | _aMood | ||
| 695 | _aMultiple signal classification | ||
| 695 | _aMusic | ||
| 695 | _aPerformance analysis | ||
| 695 | _aProduction | ||
| 695 | _aQuantization | ||
| 695 | _aReal-time systems | ||
| 695 | _aRhythm | ||
| 695 | _aRobustness | ||
| 695 | _aRocks | ||
| 695 | _aSoftware | ||
| 695 | _aStandards | ||
| 695 | _aSupport vector machine classification | ||
| 695 | _aSynchronization | ||
| 695 | _aTaxonomy | ||
| 695 | _aTiming | ||
| 695 | _aTransfer functions | ||
| 695 | _aTransient analysis | ||
| 695 | _aVisualization | ||
| 695 | _aWatermarking | ||
| 710 | 2 |
_aIEEE Xplore (Online Service), _edistributor. |
|
| 710 | 2 |
_aJohn Wiley & Sons, _epublisher. |
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| 776 | 0 | 8 |
_iPrint version: _z9781118266823 |
| 856 | 4 | 2 |
_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6266785 |
| 942 | _cEBK | ||
| 999 |
_c59839 _d59839 |
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