## 3 Experiments
### Results
#### 3.3.4 Transcription for Specific Piano  
In our proposed framework, the individual-specific transcription is conducted by feeding the output of note recognition into a note verification stage. For comparative purposes, two transcription systems were used to evaluate the performance of the proposed method. The first comparative approach was proposed by [PERSON], which is the current state-of-the-art specific piano transcription method [32]. [PERSON]'s method is implemented using a sparse NMF in AMT, and all the templates are extracted using the notes from \"ENSTDkCl\" of MAPS. Considering that the CNNs have shown advantages in the note recognition stage, the second comparative approach is based on them. Adding the specific individual's data to the training set, we got two adapted CNNs. To make a fair comparison, the newly-added training samples were isolated notes produced by the same piano. The transcription results are shown in Table 4, and the proposed method performed best in general. Although they are based on the same note recognition module, the proposed system outperformed the adapted CNNs on all evaluation metrics. This illustrates the benefits of note verification. Another reason is that the CNNs cannot learn enough information about the specific individual through these limited isolated notes--especially the information of polyphony. The proposed system outperformed [PERSON]'s system in terms of recall and F-measure. Our proposed method estimated 5511 notes correctly, whereas the number of true positive notes was 5421 for [PERSON]'s method. This can be attributed to the use of note recognition, which achieved significant performance on recall through CNNs. Meanwhile, the preliminary results led to a limitation of note verification. Both the proposed method and [PERSON]'s method achieved better performance than the adapted CNN on all evaluation metrics. One of the reasons may be that both of them use the templates of attack during the NMF. Figure 8: Results of piano transcription: (**a**) the transcription produced by CNNs in our proposed framework; and (**b**) the transcription produced by [PERSON]’s system. In general, all of the specific piano transcription systems in Table 4 perform better than universal systems in Table 3. We can conclude that making use of the information of specific individual is promising in AMT. Compared with results in Table 3, The proposed system performed better on the metrics of precision and F-measure when the note verification stage was applied. Therefore, the effectiveness of note verification is validated again. The results of the proposed method and the state-of-the-art method are compared concretely. Figure 9 shows the F-measure obtained by our proposed and [PERSON]'s methods, which is along the different octaves of a piano. As shown in Figure 9, our proposed method outperformed [PERSON]'s method for six octaves, except for the A5-Ab6 octave. [PERSON]'s method achieved an F-measure of 0.4854 for A0-Ab1, which shows its poor performance in the transcription of low-pitch notes. The proposed method showed a more balanced result, with an F-measure of 0.5672 for the first octave. In general, the F-measure increased approximately along the increase of octaves for the two methods. This suggests the limitation of the time-domain approach, which brings a time-frequency resolution trade-off. Figure 10 shows the specific piano transcription of the MAPS_MUS-bk_xmas5_ENSTDkCl excerpt, which was produced by our proposed framework and [PERSON]'s system. Compared with the ground truth in Figure 7d, the false positive notes are marked using red crosses, and the false negative notes are marked using a blue dashed line. The contrast between Figures 8a and 10a indicates that the note verification can improve the precision of transcription. As shown in Figure 10, [PERSON]'s method estimated more correct pitches than our proposed method in the excerpt's first 8.8 s. This is due to a limitation in our proposed system.