@article{Hariyanto_Suyanto_2020, title={HUM-TO-CHORD CONVERSION USING CHROMA FEATURES AND HIDDEN MARKOV MODEL}, volume={19}, url={https://computingonline.net/computing/article/view/1988}, DOI={10.47839/ijc.19.4.1988}, abstractNote={<p>Music is basically a sound arranged in such a way to produce a harmonious and rhythmic sound. The basis of music is a tone, which is a natural sound and has different frequencies for each sound. Each constant sound represents a tone. The tones can also be represented in a chord. Humans are capable of creating a sound or imitating a tone from other human beings, but they are naturally unable to represent them into musical notation without musical instruments. This research addresses a model of Hum-to-Chord (H2C) conversion using a Chroma Feature (CF) to extract the characteristics and a Hidden Markov Model (HMM) to classify them. A 10-fold cross-validating shows that the best model is represented by the chroma coefficients of 55 and HMM with a codebook of 16, which gives an average accuracy of 94.83%. Examining on a 30% testing set proves that the best model has a high accuracy of up to 97.78%. Most errors come from the chords with both high and low octaves since they are unstable. Compared to a similar model called musical note classification (MNC), the proposed H2C model performs better in terms of both accuracy and complexity.</p>}, number={4}, journal={International Journal of Computing}, author={Hariyanto, Hariyanto and Suyanto, Suyanto}, year={2020}, month={Dec.}, pages={555-560} }