A stable and accurate estimation of the fundamental frequency (pitch, F0) is an important requirement in speech and music
signal analysis, in tasks like automatic speech recognition and extraction of target signal in noisy environment. In this paper, we propose
a pitch-related spectrogram normalization scheme to improve the speaker - independency of standard speech features. A very accurate
estimation of the fundamental frequency is a must. Hence, we develop a non-parametric recursive estimation method of F0 and its 2nd and
3d harmonic frequencies in noisy circumstances. The proposed method is different from typical Kalman and particle filter methods in the
way that no particular sum of sinusoidal model is used. Also we tend to estimate F0 and its lower harmonics by using novel likelihood
function. Through experiments under various noise levels, the proposed method is proved to be more accurate than other conventional
methods. The spectrogram normalization scheme makes a mapping of real harmonic structure to a normalized structure. Results obtained for
voiced phonemes show an increase in stability of the standard speech features - the average within-phoneme distance of the MFCC features
for voiced phonemes can be decreased by several percent.
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