Hum-to-Chord Conversion Using Chroma Features and Hidden Markov Model

Abstract

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.