The nature of statistical learning theory
The nature of statistical learning theory
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Discrete Time Processing of Speech Signals
Discrete Time Processing of Speech Signals
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Signal Processing Methods for Music Transcription
Signal Processing Methods for Music Transcription
A supervised classification algorithm for note onset detection
EURASIP Journal on Applied Signal Processing
Correntropy: Properties and Applications in Non-Gaussian Signal Processing
IEEE Transactions on Signal Processing
Generalized correlation function: definition, properties, and application to blind equalization
IEEE Transactions on Signal Processing - Part I
A Pitch Detector Based on a Generalized Correlation Function
IEEE Transactions on Audio, Speech, and Language Processing
| Hi-index | 12.05 |
Fundamental frequency or pitch determination is one of the main issues in the transcription of music. In this paper, we determined the fundamental frequencies of isolated musical instrument samples by computing the correntropy functions. As the correntropy function depends on kernel methods, we demonstrated its performance using various kernel sizes. We presented the better resolution of the correntropy function than the conventional and the summary autocorrelation functions by calculating the full-width-at-half-maximum of the peaks of the functions. The superiority was confirmed for the samples of 20 different instruments based on the average width of the peaks.