Open AccessApplication of Digital Sound Recognition on Piano Tones Using MFCC and LVQ
Author(s) -
Muhammad Ardiansyah,
R F Rahmat,
Silmi Adnan,
Rina Anugrahwaty
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1235/1/012087
Subject(s) - piano , learning vector quantization , mel frequency cepstrum , speech recognition , computer science , musical , vector quantization , process (computing) , artificial intelligence , feature extraction , acoustics , art , visual arts , physics , operating system
In playing musical instruments especially the piano, a musician needs a score as the guidance to play the song. A score is a music sheet used by an artist to store or deliver a song. Many pianists do not have the expertise to create a score, especially in the making of spontaneous music. Therefore, an app is required to assist the pianists in the creation process of scores for piano music sounds. Mel-Frequency Cepstral Coefficient (MFCC) extraction method and Learning Vector Quantization method were implemented to build the application. The MFCC method was used to extract the vectors that reside in a song, while LVQ method was applied to match the testing data with the trained data. The output of this system is a musical score of the inputted song.