  Many speech and music analysis and processing schemes rely on an estimate of
the fundamental frequency $f_0$ of periodic signal components. Most established
schemes apply rather unspecific signal models such as sinusoidal models to the
estimation problem, which may limit time resolution and estimation accuracy.
This study proposes a novel time-domain locked-loop algorithm with low
computational effort and low memory footprint for $f_0$ estimation. The loop
control signal is directly derived from the input time signal, using a harmonic
signal model. Theoretically, this allows for a noise-robust and rapid $f_0$
estimation for periodic signals of arbitrary waveform, and without the
requirement of a prior frequency analysis. Several simulations with short
signals employing different types of periodicity and with added wide-band noise
were performed to demonstrate and evaluate the basic properties of the proposed
algorithm. Depending on the Signal-to-Noise Ratio (SNR), the estimator was
found to converge within 3-4 signal repetitions, even at SNR close to or below
0dB. Furthermore, it was found to follow fundamental frequency sweeps with a
delay of less than one period and to track all tones of a three-tone musical
chord signal simultaneously. Quasi-periodic sounds with shifted harmonics as
well as signals with stochastic periodicity were robustly tracked. Mean and
standard deviation of the estimation error, i.e., the difference between true
and estimated $f_0$, were at or below 1 Hz in most cases. The results suggest
that the proposed algorithm may be applicable to low-delay speech and music
analysis and processing.
