## 3 Onset Detection
### Reaching for the Piano and Beyond

Our main task was to develop a multi-classifier system strong and flexible enough that it can be tested with piano music and then easily extended to other instruments. After the exhaustive study on MPE for piano music, we extended our experiments to other instruments, starting with an electric guitar (Figure 18). We used a dataset with 400 guitar chords and single notes extracted from the guitar database, IDMT-SMT-GUITAR [61]. Seven different guitars in standard tuning were used with varying pick-up settings and different string measures to ensure a sufficient diversification in the field of electric guitars. The results depicted in Figure 18 show that the F-measure for guitar reaches 83%, which is even better than the piano results. This way, we proved that our proposed multi-classifier system-based CGP has the ability to perform MPE for other musical instruments besides piano, with even better results. ### Discussion

To discuss the quality of our proposal we have to look beyond the accuracy results. Besides the F-measure results, which have demonstrated the competitiveness of the approach not only for piano but also for electric guitars, there are a set of features that should be considered when a machine learning technique must be selected for solving a problem. We summarize some of these desirable features in Figure 11:1 \begin{table}
\begin{tabular}{c c c c c c} \hline
**Algorithm** & **Real-Time** & **Other Instruments** & **White Box** & **Piano FM (\%)** & **Guitar FM (\%)** \\ \hline Tolonem & \(\surd\) & x & x & 47\% & - \\ \hline Tolonen-500 & \(\surd\) & x & x & 61\% & - \\ \hline
**CGP** & \(\surd\) & \(\surd\) & \(\surd\) & **76\%** & **83\%** \\ \hline [PERSON] & x & \(\surd\) & x & 80\% & - \\ \hline Klapuri & x & \(\surd\) & x & 82\% & - \\ \hline \end{tabular}
\end{table} 
Table 2: The algorithm’s main features. Figure 18: F-measure results for MPE on 2 different instruments: for piano, the F-measure is 76%, and for guitar, it rises to 83%. Our CGP algorithm for MPE is unique in terms of the technique used to address the problem we face. It is the first one that embodies genetic programming and a distinctive architecture with multiple classifiers that can work independently and in parallel. As far as we know, there is no other approach for this kind of problem with a similar multi-classifier architecture. In fact, our architecture is highly parallelizable: since the evolved classifiers are independent, they can run in parallel. Our system has been preliminary tested on a 8 core processor and, due to its parallelization capabilities, it works in real time. Therefore, as shown in the table, our CGP-based approach is among real-time techniques that provides the best F-measures. We easily extended our CGP system to other instruments with accurate results. We trained and tested our system for electric guitars and we reached an outstanding 83% F-measure. This suggests that this is the only real-time technique developed for piano that may be applied without changes to any instrument. Although we have successfully tested it for a guitar, we hope the results will be positive with other instruments, given that, after piano, the guitar is another instrument with a high polyphony degree. One of the most important features that stands out from our system is the white-box method that allows us to study the solutions provided by avoiding opacity and by allowing future improvements based on what we can learn. Again, this is the only technique among state-of-the-art techniques featuring white-box optimization. Summarizing, as shown in Table 2, the CGP-based system is the only one that provides a real-time approach, provides white-box optimization, is easily extendable to other instruments, has competitive F-measure for piano music, and has outstanding F-measure values for guitar music.