Octave Error Reduction in Pitch Detection Algorithms Using Fourier Series Approximation Method

Abstract

ABSTRACTPitch detection has been an area of research in the field on Music Information retrieval, which has always received great attention. Pitch is defined as the frequency of a pure tone signal which the brain perceives while listening to a signal. Pitch detection methods ideally identify the fundamental frequency (f0) or fundamental time period (T) of a signal and f0 is defined as the inverse of T. Several pitch detection algorithms have been proposed in the past and each one has its advantages and disadvantages. One of the prominent problems seen across all pitch detection algorithms is to identify the correct octave to which the pitch or f0 belongs to. Ideally, we are required to find the fundamental harmonic. Due to the structure of vocal tract and the acoustic nature of the instruments and due to various other factors like noise, the speech, and music signals are not perfectly periodic in nature and are termed as quasi-periodic signals. In this paper, we have proposed a method using Fourier series to approximate the quasi-periodic signals with a periodic signal, which analyses the strengths of various harmonics of the signal to arrive at a better estimate of the pitch for monophonic sounds. The brain perceives a periodic signal even while listening to a quasi-periodic signal. Hence, the idea is to approximate the quasi-periodic signal with a perfectly periodic signal and estimate the fundamental frequency of the signal which gives the best approximation. The method was tested on synthetic signals and real music signals and found to improve the pitch detection accuracy.