Julian Vogels

Automatic Music Transcription: Anssi Klapuri’s fundamental frequency 
estimation algorithms

For Ichiro Fujinaga‘s Music Technology class “MUMT 621 – Music Information Acquisition, Preservation, and Retrieval” I prepared this Keynote presentation about automatic music transcription with emphasis on the work of Anssi Klapuri, especially on his 2006 paper “Multiple Fundamental Frequency Estimation by Summing Harmonic Amplitudes”. You can download it in .key format or as a PDF file (without animations).

 

 

Annotated Bibliography

Klapuri, Anssi P. 2006. Multiple Fundamental Frequency Estimation by Summing Harmonic Amplitudes. University of Victoria.
The main paper of the presentation: Estimation by calculating the salience.
Klapuri, Anssi P. 2004.Signal Processing Methods for the Automatic Transcription of Music. PhD Thesis, Tampere University of Technology, Tampere, Finland.
Anssi Klapuri’s PhD Thesis rewiewing multiple F0-estimation algorithms and presenting/reviewing his own algorithms (some of them were already published, some were new) along with musical meter estimation.
Klapuri, Anssi P. 2004.Automatic Music Transcription as We Know it Today. Journal of New Music Research 33 (3):269-282.
An overview over the field of automatic music transcription from 2004.
Raphael, Christopher. 2002. Automatic Transcription of Piano Music. IRCAM – Centre Pompidou, France.
A Hidden Markov Model approach to piano music transcription.
Poliner, Graham E., and Daniel P. W. Ellis. 2007. Improving Generalization For Polyphonic Piano Transcription. In IEEE Workshop on Applications of Signal Processing to Audio and Acoustics. New Paltz, NY.
A Support Vector Machines approach to piano music transcription.
Moorer, James A. 1975. On the Segmentation and Analysis of Continuous Musical Sound by Digital Computer. Center for Computer Research in Music and Acoustics, Stanford University.
The original paper of the field, introducing several signal analysis methods such as extracting individual harmonics with bandpass filtering.
Smaragdis, Paris, and Judith C. Brown. 2003. Non-Negative Matrix Factorization for Polyphonic Music Transcription. In IEEE Workshop on Applications of Signal Processing to Audio and Acoustics. New Paltz, NY.
A Non-negative matrix decomposition method to estimate the spectral profile and temporal information (not knowledge-based).
Marolt, Matija. 2001. A Connectionist Approach to Automatic Transcription of Polyphonic Piano Music. Faculty of Computer and Information Science, University of Ljubljana.
A Neural Networks approach to automatic transcription of piano music.
Ryynänen, Matti P., and Anssi P. Klapuri. 2005. Polyphonic Music Transcription using Note Event Modeling. In IEEE Workshop on Applications of Signal Processing to Audio and Acoustics. New Paltz, NY.
A Hidden Markov Model approach to a real-world signal transcription, searching for different paths through note models.
Chang, Wei-Chen, Alvin W. Y. Su, Chunghsin Yeh, Axel Roebel, and Xavier Rodet. 2008. Multiple-F0 Tracking Based on a High-Order HMM Model. In IEEE Workshop on Applications of Signal Processing to Audio and Acoustics. New Paltz, NY.
Another Hidden Markov Model approach to F0-tracking involving a rather complex tracking mechanism.
Benetos, Emmanouil, Simon Dixon, Dimitri Giannoulis, Holger Kirchhoff, and Anssi P. Klapuri. 2012. Automatic Music Transcription: Breaking the Glass Ceiling. Paper read at International Society for Music Information Retrieval Conference (ISMIR), at Porto, Portugal.
A recent paper on audio-to-midi transcription, reviewing the field and pledging for use-case tailored algorithms.
Paleari, Marco, Benoit Huet, Anthony Schutz, and Dirk Slock. 2008. A Multimodal Approach to Music Transcription. In 15th IEEE International Conference on Image Processing, 2008.
A multimodal approach to (e.g., guitar) transcription, that fuses the audio information and the visual data stream from a video to support traditional transcription techniques.
Martin, Keith D. 1996. A Blackboard System for Automatic Transcription of Simple Polyphonic Music.
This technical report describes the benefits and limitations of a blackboard architecture approach to automatic piano music transcription.
  Posted by julian.vogels in Work on March 12, 2013

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