Chord Recognition in Music Using a Robust Pitch Class Profile (PCP) Feature and Support Vector Machines (SVM)

Chord Recognition in Music Using a Robust Pitch Class Profile (PCP) Feature and Support Vector Machines (SVM)

Robustness, PCP Feature, SVM, Music Chord

Music serves as a powerful and immediate avenue for the expression of emotions, and a nuanced understanding of musical compositions is crucial for accurately interpreting and appreciating them. This research centers on the examination of robust Pitch Class Profile (PCP) features and Support Vector Machine (SVM) in the realm of music analysis. The initial phase of the study delves into the exploration of pertinent concepts and a myriad of resilient Constant-Q Transform (CQT) methods used in describing chord spectra for audio analysis. Subsequently, the paper elucidates the intrinsic correlation between SVM and speech tonality, outlining the design of a comprehensive system for music chord recognition. Rigorous testing of the system's performance follows, with a particular emphasis on evaluating the recognition rate. The results of these tests underscore the significant enhancement in music chord recognition achieved by the system, highlighting the pivotal role played by robust feature optimization and SVM pattern in bolstering its efficacy. This research not only contributes to the theoretical understanding of music analysis but also provides practical insights into improving the accuracy of music chord recognition systems through innovative feature selection and machine learning techniques.

Suwatchai Kamonsantiroj1,*, Lita Wannatrong2, Luepol Pipanmaekaporn3

2024

peri irawan

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