ABSTRACT
This paper presents a new method for multi-pitch estimation on piano recordings. We propose a framework based on a set of classifiers to analyze the audio input and identify the piano notes present on the given audio signal. Our system's classifiers were evolved using Cartesian Genetic Programming: we take advantage of Cartesian Genetic Programming to evolve a set of mathematical functions that act as independent classifiers for piano notes. Our latest improvements are also presented, including test results using F-measure metrics. Our system architecture is also described to show the feasibility of its parallelization and implementation as a real time system. The proposed approach achieved competitive results, when compared to the state of the art.
- Valentin Emiya, Roland Badeau, and Bertrand David. 2010. Multipitch estimation of piano sounds using a new probabilistic spectral smoothness principle. Audio, Speech, and Language Processing, IEEE Transactions on 18, 6 (2010), 1643--1654.Google ScholarDigital Library
- Valentin Emiya, Nancy Bertin, Bertrand David, and Roland Badeau. 2010. MAPS - A piano database for multipitch estimation and automatic transcription of music. Research Report. 11 pages. https://hal.inria.fr/inria-00544155Google Scholar
- Brian W Goldman and William F Punch. 2015. Analysis of cartesian genetic programming's evolutionary mechanisms. IEEE Transactions on Evolutionary Computation 19, 3 (2015), 359--373.Google ScholarDigital Library
- Nikolaus Hansen, Dirk V. Arnold, and Anne Auger. 2015. Evolution Strategies. Springer Berlin Heidelberg, Berlin, Heidelberg, 871--898. Google ScholarCross Ref
- Simon Harding, Jürgen Leitner, and Juergen Schmidhuber. 2013. Cartesian genetic programming for image processing. In Genetic programming theory and practice X. Springer, 31--44.Google Scholar
- Tiago Inácio, Rolando Miragaia, Gustavo Reis, Carlos Grilo, and Francisco Fernandéz. 2016. Cartesian genetic programming applied to pitch estimation of piano notes. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 1--7.Google ScholarCross Ref
- A. P. Klapuri. 2003. Multiple fundamental frequency estimation based on harmonicity and spectral smoothness. IEEE Transactions on Speech and Audio Processing 11, 6 (Nov 2003), 804--816. Google ScholarCross Ref
- John R Koza. 1992. Genetic programming: on the programming of computers by means of natural selection. Vol. 1. MIT press.Google ScholarDigital Library
- John R Koza. 1994. Genetic programming II: Automatic discovery of reusable subprograms. Cambridge, MA, USA (1994).Google ScholarDigital Library
- M. Marolt. 2004. A connectionist approach to automatic transcription of polyphonic piano music. IEEE Transactions on Multimedia 6, 3 (June 2004), 439--449. Google ScholarDigital Library
- Luis Gustavo Martins. 2008. A computational Framework for Sound Segregation Music Signals. Ph.D. Dissertation. University of Porto, Porto, Portugal.Google Scholar
- J.F. Miller. 2011. Cartesian Genetic Programming. 17--33 pages. Google ScholarCross Ref
- Julian F Miller. 2013. GECCO 2013 tutorial: cartesian genetic programming. In Proceedings of the 15th annual conference companion on Genetic and evolutionary computation. ACM, 715--740.Google ScholarDigital Library
- Julian Francis Miller and Simon L Harding. 2008. Cartesian genetic programming. In Proceedings of the 10th annual conference companion on Genetic and evolutionary computation. ACM, 2701--2726.Google ScholarDigital Library
- Rolando Miragaia, Gustavo Reis, Francisco Fernandéz, Tiago Inácio, and Carlos Grilo. 2018. CGP4Matlab-A Cartesian Genetic Programming MATLAB Toolbox for Audio and Image Processing. In International Conference on the Applications of Evolutionary Computation. Springer, 455--471.Google ScholarCross Ref
- M. Mueller and F. Wiering (Eds.). 2015. An efficient temporally-constrained probabilistic model for multiple-instrument music transcription. ISMIR, Malaga, Spain.Google Scholar
- A Michael Noll and Manfred R Schroeder. 1964. Short-Time "Cepstrum" Pitch Detection. The Journal of the Acoustical Society of America 36, 5 (1964), 1030--1030.Google ScholarCross Ref
- Alan V Oppenheim and Ronald W Schafer. 2004. From frequency to quefrency: A history of the cepstrum. IEEE signal processing Magazine 21, 5 (2004), 95--106.Google Scholar
- G Reis, F Fernandéz de Vega, and A Ferreira. 2012. Audio Analysis and Synthesis-Automatic Transcription of Polyphonic Piano Music Using Genetic Algorithms, Adaptive Spectral Envelope Modeling, and Dynamic Noise Level Estimation. IEEE Transactions on Audio Speech and Language Processing 20, 8 (2012), 2313.Google ScholarDigital Library
- Gustavo Reis, Nuno Fonseca, Francisco Fernandez, and Aníbal Ferreira. 2008. A genetic algorithm approach with harmonic structure evolution for polyphonic music transcription. In Signal Processing and Information Technology, 2008. ISSPIT 2008. IEEE International Symposium on. IEEE, 491--496.Google ScholarCross Ref
- Gustavo Reis, Nuno Fonseca, Francisco Fernández de Vega, and Anibal Ferreira. 2008. Hybrid genetic algorithm based on gene fragment competition for polyphonic music transcription. Applications of Evolutionary Computing (2008), 305--314.Google Scholar
- Chunghsin Yeh, Axel Roebel, and Xavier Rodet. 2010. Multiple Fundamental Frequency Estimation and Polyphony Inference of Polyphonic Music Signals. Trans. Audio, Speech and Lang. Proc. 18, 6 (Aug. 2010), 1116--1126. Google ScholarCross Ref
Index Terms
Evolving a multi-classifier system with cartesian genetic programming for multi-pitch estimation of polyphonic piano music
Recommendations
Short-term memory and event memory classification systems for automatic polyphonic music transcription
CSECS'09: Proceedings of the 8th WSEAS International Conference on Circuits, systems, electronics, control & signal processingMusic transcription consists in transforming the musical content of audio data into a symbolic representation. The objective of this study is to investigate a transcription system for polyphonic piano. The input to this system consists in piano music ...
Towards Complete Polyphonic Music Transcription: Integrating Multi-Pitch Detection and Rhythm Quantization
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)Most work on automatic transcription produces “piano roll” data with no musical interpretation of the rhythm or pitches. We present a polyphonic transcription method that converts a music audio signal into a human-readable musical score, by ...
Comments