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Audio Content Analysis

Inhalt: 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 der Vorlesung: 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: 10.04.2012 - 11.07.2012

Wochentag / Zeit d. Vorlesung: Mo, 18-20 Uhr
Wochentag / Zeit d. Übung:
Mi, 16-18 Uhr
Raum d. Vorlesung: TA 201
Raum d. Übung: H 3017

Dozenten und Tutoren

Dozenten: Dr. Alexander Lerch und Dipl. Ing. Tim Flohrer

Prüfung

Projektpräsentation und Ausarbeitung

Weitere Informationen

Veranstaltungstyp:
Umfang: 4 SWS (VL+UE)
Angebot: jedes 2. Semester
Voraussetzung: Kenntnisse der digitalen Audiosignalverarbeitung, Matlabkenntnisse
Veranstaltungsnummer: 0135 L XXX

Material und Skripte

Introduction:

  • J. Stephen Downie: Music Information Retrieval, In: Blaise Cronin (Ed.), Annual Review of Information Science and Technology, Vol: 37, Information Today Books, pp 295-340, 2003 [1]
  • Nicola Orio: Music Retrieval: A Tutorial and Review, Foundations and Trends® in Information Retrieval: Vol. 1: No 1, pp 1-90, 2006. [2]


Fundamentals:

  • Julius O. Smith: Mathematics of the Discrete Fourier Transformation with Audio Applications, 2nd Edition [3]
  • Julius O. Smith: Introduction to Digital Filters with Audio Applications [4]
  • Julius O. Smith: Spectral Audio Signal Processing, March 2007 Draft [5]


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) [6]


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 [7]
  • Dixon, Simon: Onset Detection Revisited, Proc. of the 9th International Conference on Digital Audio Effects (DAFx), 2006 [8]
  • MIREX 2006: Audio Onset Detection Evaluation [9]


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 [10]
  • 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 [11]
  • 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 [12]
  • 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 [13]
  • 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 [14]
  • Peeters, Geoffrey:  Time variable tempo detection and beat marking. In: Proc. of the International Computer Music Conference (ICMC). Barcelona, September 2005 [15]
  • MIREX 2006: Audio Beat Tracking Evaluation [16]


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 [17]
  • 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 [18]


Polyphonic Pitch Tracking:

  • Cheveigné, Alain de ; Kawahara, Hideki: Multiple period estimation and pitch perception model. In: Speech Communication 27 (1999), pp. 175–185 [19]
  • 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 [20]
  • 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 [21]


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 [22]
  • 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 [23]


Key Estimation:

  • Izmirli, Özgür: Template based key finding from audio. In: Proc. of the International Computer Music Conference (ICMC). Barcelona, September 2005 [24]
  • 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 [25]


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 [26]
  • 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 [27]


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 [28]
  • 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 [29]


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 [30]
  • 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 [31]
  • 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 [32]


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 [33]


Music Performance Analysis
:

  • Lerch, Alexander: Software-based Extraction of Objective Parameters from Music Performances, PhD Thesis, Technische Universität Berlin, 2008 [34]
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