## 3 Onset Detection
## 7 Conclusions and Future Work

We proposed in this paper a multi-pitch estimation system for piano music based on Cartesian genetic programming using an harmonic mask and improved with a data augmentation process for a better training stage. We presented a detailed description of all the system features, which perform multi-pitch estimation using Cartesian genetic programming, harmonic masks, and a data augmentation process. We also introduced an onset detection analytic algorithm to aid the system working process. The main disadvantage of our proposal is the fact that one first needs to evolve the 61 classifiers, so that the system is able to employ those classifiers to perform transcription and so that the training (evolutionary) process takes time: each single classifier takes one day to train (ten runs). However, our technique produces competitive results for any type of piano sound without the need for harmony rules for chords construction. This way, it is more generalized and works with random notes, which could be of interest for contemporary music. Moreover, we have also shown that the methodology can be applied without changes to other polyphonic instruments, such as the guitar. The CGP-HM-DA approach based on a graphs of nodes with functions extracted from a dataset can be easily decoded once the classifiers are evolved for a given instrument: the classifiers are made up of mathematical expressions with inputs, internal functions, and outputs, which provide _white-box_ optimization. The results are shown and compared with state-of-the-art algorithms, and we demonstrated the feasibility of the approach: this technique is the only one providing simultaneously (i) competitive results (F-measure) together with (ii) real-time capabilities thanks to their intrinsic parallel nature; (iii) white-box optimization; (iv) and direct application to other polyphonic instruments with outstanding results (F-measure). Future work will focus on demonstrating the capabilities of the proposed system on other types of pitched instruments, namely woodwind and string ensembles. We also look forward to proposing a similar system for non-pitched instruments, such as the drums. Given the parallel nature of our approach and its ability to perform in real-time, we also aim to implement a real-time improvisation music composition system, using the proposedalgorithm as the system input. Here, the goal is to create a system that is capable of interacting through its music and to play with other musicians. Conceptualization, investigation, and methodology, R.M., F.F. and G.R. ; software, R.M. and T.I. ; supervision, F.F. and G.R. ; funding acquisition, F.F. ; writing--original draft preparation, R.M., F.F. and G.R. ; writing--review and editing, F.F. and G.R. All authors have read and agreed to the published version of the manuscript. This research was funded by the Spanish Ministry of Economy and Competitiveness under project TIN2017-85727-C4-[2,4]-P; by the Regional Government of Extremadura, Department of Commerce and Economy, the European Regional Development Fund, a way to build Europe, under project IB16035; and by Junta de Extremadura, project GR15068 and project GR18049. Not applicable. The authors would like to thank to [PERSON] for providing powerful hardware at Polytechnic of Leiria to perform important tests; [PERSON] for his hard work on configuring the blade machines at the Universidad de Extremadura, Merida, so that we could perform tests; and [PERSON] for technical support on installing and configuring software and virtualization solutions. We also would like to thank CIC--Computer Science and Communication Research Centre--for the logistic support and co-funding by FCT--Fundacao para a Ciencia e Tecnologia, I.P., under the projectUIDB/04524/2020. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: