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In the second phase, an attack is reported if, at a certain frame, the loudness level is greater than the loudness threshold and the norm of the differential curve exceeds the differential threshold. This frame is considered as the “attack frame’’. Figure 2. The attack detector: on the left, the full sound and attack portion. On the right, a zoom of the pre-attack and post-attack portions of the signal.

Figure 2 In the second phase, an attack is reported if, at a certain frame, the loudness level is greater than the loudness threshold and the norm of the differential curve exceeds the differential threshold. This frame is considered as the “attack frame’’. Figure 2. The attack detector: on the left, the full sound and attack portion. On the right, a zoom of the pre-attack and post-attack portions of the signal.

Related Figures (6)

Figure 1. The gestures to produce the six basic Pandeiro sounds. The need for automatically analyzing Pandeiro sounds is two- fold. First, MIR applications, for education notably, require the ability to automatically transcribe Pandeiro solos.
Figure 1. The gestures to produce the six basic Pandeiro sounds. The need for automatically analyzing Pandeiro sounds is two- fold. First, MIR applications, for education notably, require the ability to automatically transcribe Pandeiro solos.
Figure 3. Results on full sounds. IGR stands for Information Gain Ratio. EDS FS denotes our fea- ture selection algorithm based on the F-measure. Train/Test denotes the experiment in which the classifier is trained on the training database and tested on the test database. 10-fold XV denotes the 10-fold cross validation experiment on the test database.
Figure 3. Results on full sounds. IGR stands for Information Gain Ratio. EDS FS denotes our fea- ture selection algorithm based on the F-measure. Train/Test denotes the experiment in which the classifier is trained on the training database and tested on the test database. 10-fold XV denotes the 10-fold cross validation experiment on the test database.
Figure 4. Results obtained with on pre-attacks. See above for abbreviations. The “Signal” lin gives the performance of classifiers using the input signal directly as a feature.
Figure 4. Results obtained with on pre-attacks. See above for abbreviations. The “Signal” lin gives the performance of classifiers using the input signal directly as a feature.
Figure 6. Analytical vs. reference features on attacks
Figure 6. Analytical vs. reference features on attacks
Figure 5. Results obtained with on post-attacks. See above for abbreviations.
Figure 5. Results obtained with on post-attacks. See above for abbreviations.
A precise description of operators can be found in [46]. 7.2. Annex 2 — Reference features
A precise description of operators can be found in [46]. 7.2. Annex 2 — Reference features
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Interactive MusicAudio Classification
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