Annotated Bibliography for the Class Presentation on Automated Transcription of Polyphonic Piano Music

Catherine Lai


Dixon, S. 2000. On the Computer Recognition of Solo Piano Music. Australasian Computer Music Conference. 31-7.

Available: (Last accessed February 17, 2005)


This paper is one of the easier reading papers on the work towards a computer system for the automatic transcription of piano performances. Stanadard signal processing techniques based on the short time Fourier transform are used to create time-frequency representation and classi peak findg algorithms are described.




Marolt, M. 2004. A connectionist approach to automatic transcription of polyphonic piano music. IEEE Transactions on Multimedia 6, no. 3 (June): 439-49.

Available: clanki/ieee.tmm.transcription.pdf (Last accessed February 17, 2005)


Marolt published many papers on the topic of automated transcription system for piano music, but this is the best written in terms of the technical detail and organization of the paper. This time he presented a connectionist approach to automatic transcription of polyphonic piano music using several neural networks where a new model for tracking partials in polyphonic audio signal based on networks of adaptive oscillators was proposed.




Martin, K. 1996. A blackboard system for automatic transcription of simple polyphonic music. MIT Media Laboratory Perceptual Computing Section Technical Report No. 385.

Available: (Last accessed February 17, 2005)


This paper is cited in many of the related works areas in papers. Martin's piano transcription allowed up to four voices in the input data; however, it was restricted to the chorale style of J.S. Bach, where notes have relatively long duration and change simultaneously .




Monti, G, and M. Sandler. 2002. Automatic Polyphonic Piano Note Extraction Using Fuzzy Logic in a Blackboard System. Proceedings of the International Conference on Digital Audio Effects. 39-44.

Available: papers/DAFX02_Monti_Sandler_polyphonic_piano_extraction.pdf (Last accessed February, 17, 2005)


Monti and Sandler presented the implementation of Polyphonic Note Recognition using a Fuzzy Inference System (FIS) as part of many of the processes called Knowledge Sources (KSs) in a Blackboard system. The use of this technique is considered to be one of the most robust among all the various approaches taken by different research groups. I find the paper very hardest to read, especially when it ends the with a result section without any summary of the overall system or any concluding remarks.




Moorer, J. 1975. On the segmentation and analysis of continuous musical sound by digital computer. Ph.D. thesis, Stanford University, CCRMA.


This is always cited to be the pioneering work in the area of polyphonic transcription, and the fact that the system has limitations on note range and only supports two different timbre voices is always mentioned.




Raphael, C. 2002. Automatic Transcription of Piano Music. Proceedings of the International Conference on Music Information Retrieval.

Available: (Last accessed February 17, 2005)


This paper uses the HMM approach.It seems difficult at first, but after several times of reading the material is not all incomprehensible, as in the case of Monti's.