Pitch Estimation Using Non-negative Matrix Factorization

The problem of pitch detection consists of estimating the dominant frequency present in a certain time window. This paper demonstrates and analyzes the use of a non-negative matrix factorization technique with a frequency basis formed with a correntropy kernel. This offers the advantage that the frequency basis is adaptable, allowing the matrix factorization to fit the data precisely, as well as including a dictionary specifically to account for noise. Using non-negative matrix factorization also allows an increase in dimensionality, which increases the frequency resolution of the algorithm. The method is tested on a database of trumpet notes and compared to other current methods, improving on their performance for noisy signals.