| dc.contributor.author | Nurhayat, Yosi | |
| dc.contributor.author | Buono, Agus | |
| dc.date.accessioned | 2016-05-23T05:12:04Z | |
| dc.date.available | 2016-05-23T05:12:04Z | |
| dc.date.issued | 2013-11 | |
| dc.identifier.issn | 2338-7718 | |
| dc.identifier.uri | http://repository.ipb.ac.id/handle/123456789/80648 | |
| dc.description.abstract | The guitar is a musical instrument that has (I chord (IS a reference tone. II is afact that
is nol all human auditorv svstem Ca/1 distinguish between high and low tones of a
musical instrument in good accurate. TheII, in this research Ire develop 0 voice guitar
10 coni conversion using resilient backproagation neural network (RBNN) as 10
classifier and MeI Frequency Cepstrum Coefficient (MFCC) as feature extraction. We
record 345 for each cord (totallv we have 8640 recording data with WAV format)..
Experiments are conductedfor some number (!l cepstral coefficients (13, 26. and 39).
with 100 millisecond as time Fame and 40% overlapping between successive Fame.
Total number ofhidden neurons in RBNN model in this experiments are 10, 25. 50 and
1OO. According tu the experiment. the maximum accuracy is 96,88%for 52 number of
cepstral coefficients and 100 neurons hidden | id |
| dc.language.iso | id | id |
| dc.publisher | other | id |
| dc.publisher | other | id |
| dc.title | PEMODELAN JARINGAN SYARAF TIRUAN RESILIENT BACKPROPAGATION UNTUK KONVERSI SUARA GITAR KECORD | id |
| dc.subject.keyword | Chord | id |
| dc.subject.keyword | Resilient Backpropagation Neural Network | id |
| dc.subject.keyword | MeI Frequency cepstral Coefficients (MFCC). | id |
