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Instrument recognition may be dependent on the energy per octave.
Instrument recognition is a typical supervised classification task.
For instrument recognition, we adapt common timbral features for instrument recognition by extracting them channel-wise from the auditory output.
Table 12 shows the result of feature selection for instrument recognition.
Supervised classification is required for our approaches in pitch estimation and instrument recognition.
Table 10b shows the evaluation of the experimental design for predominant instrument recognition.
We will look at the results of onset detection, pitch estimation, and instrument recognition, consecutively.
(iii Improving frequency representation with Smt mapping may improve instrument recognition compared to the Std mapping.
The error rates for instrument recognition are listed in Table 11.
They relied heavily on manual instrument recognition to determine the appropriate position of each channel.
Experimental results indicate the applicability and effectiveness of a RSN to perform accurate and robust surgical instrument recognition.
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