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Inhalt des Dokuments

Klanganalyse und Synthese: Audio Content Analysis

Die Analyse von Audiosignalen zur Extraktion von
musikalischen Merkmalen wie Melodie, Tempo, Genre, etc. erfordert
angepaßte Ansätze der digitalen Audiosignalverarbeitung. Diese Vorlesungs-Übungskombination stellt Lösungsansätze vor, die dann
praktisch in der begleitenden Matlabübung umgesetzt werden können.

Inhalt des Seminars: Vorstellung von Algorithmen der digitalen Signalverarbeitung zur Analyse von Audiodaten wie Tonhöhenerkennung, Tempoerkennung, etc.
Dozent: Dr. Alexander Lerch

Inhalt der Übung: Praktische Übung zur Umsetzung von Algorithmen zur Signalanalyse in Matlab.
Dozent: Dipl.-Ing. Tim Flohrer

Zeitraum und Ort

Zeitraum: 22.04.2008- 15.07.2008
Wochentag / Zeit Seminar: Mittwoch 16-18 Uhr
Wochentag / Zeit Übung: Donnerstag 16-18 Uhr
Raum Seminar: EN 327
Raum Übung: EN 327

Dozenten und Tutoren

Dozent: Dr. Alexander Lerch [1], Dipl.-Ing. Tim Flohrer [2]

Prüfung

Projektpräsentation und Ausarbeitung

Weitere Informationen

Veranstaltungstyp: Seminar, Übung
Umfang: 4 SWS
Angebot im: Sommersemester 2009
Voraussetzung: Kenntnisse der digitalen Audiosignalverarbeitung, Matlabkenntnisse
Veranstaltungsnummer: 0135 L XXX

Material

Introduction:


Fundamentals:

  • Julius O. Smith: Mathematics of the Discrete Fourier Transformation with Audio Applications, 2nd Edition [4]
  • Julius O. Smith: Introduction to Digital Filters with Audio Applications [5]
  • Julius O. Smith: Spectral Audio Signal Processing, March 2007 Draft [6]


Low Level Features
:

  • Peeters, Geoffroy:  A large set of audio features for sound description (similarity and classification) in the CUIDADO project / IRCAM. 2004. – Project Report (CUIDADO) [7]


Onset Detection
:

  • Bello, J.P.   Daudet, L.   Abdallah, S.   Duxbury, C.   Davies, M.   Sandler, M.B.: A Tutorial on Onset Detection in Music Signals, IEEE Trans. on Speech and Audio Processing 13(5), 2005 [8]
  • Dixon, Simon: Onset Detection Revisited, Proc. of the 9th International Conference on Digital Audio Effects (DAFx), 2006 [9]
  • MIREX 2006: Audio Onset Detection Evaluation [10]


Beat Tracking
:

  • Large, Edward W.:  Beat Tracking with a Nonlinear Oscillator. In: Proc. of the 14thInternational Joint Conference on Artificial Intelligence (IJCAI). Montreal, August 1995 [11]
  • Goto, Masataka ; Muraoka, Yoichi:  Music Understanding At The Beat Level – Real-time Beat Tracking For Audio Signals.  In:  Proc. of the Workshop on ComputationalAuditory Scene Analysis (IJCAI), 1995 [12]
  • Scheirer, Eric D.:  Tempo and beat analysis of acoustic musical signals. In: Journal of the Acoustical Society of America (JASA) 103 (1998), No. 1, pp. 588–601 [13]
  • Dixon, Simon:  A Lightweight Multi-Agent Musical Beat Tracking System. In: Proc. of the Pacific Rim International Conference on Artificial Intelligence (PRICAI). Melbourne, August/September 2000 [14]
  • Gouyon, Fabien ; Herrera, Perfecto:  A Beat Induction Method for Musical Audio Signals.  In:  Proc. of the 4th European Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS). London, April 2003 [15]
  • Peeters, Geoffrey:  Time variable tempo detection and beat marking. In: Proc. of the International Computer Music Conference (ICMC). Barcelona, September 2005 [16]
  • MIREX 2006: Audio Beat Tracking Evaluation [17]


Monophonic Pitch Tracking
:

  • Cuadra, Patricio de la ; Master, Aaron ; Sapp, Craig: Efficient Pitch Detection Techniques for Interactive Music, In: Proc. of the International Computer Music Conference (ICMC), Habana, 2001 [18]
  • Cheveigne, Alain de ; Kawahara, Hideki: YIN, a fundamental frequency estimator for speech and music, In: Journal of the Acoustical Society of America (JASA) 111 (2002), No. 4, pp. 1917–1930 [19]


Polyphonic Pitch Tracking:

  • Cheveigné, Alain de ; Kawahara, Hideki: Multiple period estimation and pitch perception model. In: Speech Communication 27 (1999), pp. 175–185 [20]
  • Karjalainen, Matti ; Tolonen, Tero: Multi-pitch and periodicity analysis model for sound separation and auditory scene analysis. In: Proc. of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP). Phoenix, March 1999 [21]
  • Klapuri, Anssi P.: A Perceptually Motivated Multiple-F0 Estimation Method. In:
    Proc. of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA). New Paltz, October 2005 [22]


Tuning Frequency Estimation
:

  • Lerch, Alexander: On the Requirement of Automatic Tuning Frequency Estimation, In: Proc. of the 7th International Conference on Music Information Retrieval (ISMIR). Victoria, October 2006 [23]
  • Dressler, Karin ; Streich, Sebastian: Tuning Frequency Estimation using Circular Statistics. In: Proc. of the 8th International Conference on Music Information Retrieval (ISMIR). Wien, September 2007 [24]


Key Estimation:

  • Izmirli, Özgür: Template based key finding from audio. In: Proc. of the International Computer Music Conference (ICMC). Barcelona, September 2005 [25]
  • Peeters, Geoffroy: Chroma-based estimation of musical key from audio-signal analysis, In: Proc. of the 7th International Conference on Music Information Retrieval (ISMIR). Victoria, October 2006 [26]


Chord Detection:

  • Bello, Juan Pablo ; Pickens, Jeremy:  A Robust Mid-level Representation for Harmonic Content in Music Signals.   In:  Proc. of the 6th International Conference on Music Information Retrieval (ISMIR). London, September 2005 [27]
  • Papadopoulos, Hélène ; Peeters, Geoffroy:  Large-scale study of chord estimation algorithms based on chroma representation and HMM.  In: Proc. of the International Workshop on Content-Based Multimedia Indexing (CBMI). Bordeaux, 2007 [28]


Audio-to-Audio Alignment:

  • Hu, Ning ; Dannenberg, Roger B. ; Tzanetakis, George:  Polyphonic Audio Matching and Alignment for Music Retrieval. In: Proc. of the IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA). New Paltz, October 2003 [29]
  • Turetsky, Robert J. ; Ellis, Daniel P. W. :  Ground-Truth Transcriptions of Real
    Music from Force-Aligned MIDI Syntheses
    . In: Proc. of the 4th International Conference on Music Information Retrieval (ISMIR). Baltimore, October 2003 [30]


Musical Genre Classification:

  • Tzanetakis, George ; Cook, Perry:  Musical Genre Classification of Audio Signals. In: Trans. on Speech and Audio Processing 10 (2002), No. 5, pp. 293–302 [31]
  • Burred, Juan José ; Lerch, Alexander:  A hierarchical approach to automatic musical genre classification. In: Proc. of the 6th Int. Conference on Digital Audio Effects (DAFx). London, September 2003 [32]
  • Scaringella, Nicola ; Zoia, Giorgio ; Mlynek, Daniel:  Automatic genre classification of music content: a survey . In: Signal Processing Magazine 23 (2006), No. 2, pp. 133–141 [33]


Audio Fingerprinting:

  • Cano, Pedro ; Batlle, Eloi ; Kalker, Ton ; Haitsma, Jaap:  A Review of Audio
    Fingerprinting
    . In: The Journal of VLSI Signal Processing 41 (2005), No. 3, pp. 271–284 [34]


Music Performance Analysis
:

  • Lerch, Alexander: Software-based Extraction of Objective Parameters from Music Performances, PhD Thesis, Technische Universität Berlin, 2008 [35]


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