Title :
Periodicity estimation by hypothesis-directed search
Author_Institution :
M.I.T. Artificial Intelligence Laboratory, Cambridge, Mass.
Abstract :
Some methods are described for estimating the fundamental periodicity of additively combined quasi-periodic signals. These methods operate from measurements of the instantaneous amplitudes and frequencies of important sinusoidal components of the input signal. The methods share a similar computational structure, in which each component is allowed to assert a number of hypotheses as to possible fundamental periodicities to which it is related. Hypotheses from different components are combined in such a way as to reinforce common periodicities shared by several components. The methods discussed seem well suited to signals whose fundamental frequencies may be high, and vary over a wide range.
Keywords :
Artificial intelligence; Frequency estimation; Frequency measurement; Humans; Laboratories; Multiple signal classification; Music; Signal analysis; Signal resolution; Speech analysis;
Conference_Titel :
Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP '85.
DOI :
10.1109/ICASSP.1985.1168414