We consider the problem of detecting an unknown signal from an unknown noise type. We restrict the signal type to a class of slowly varying periodic signalswith harmonic components, a classwhich includes real signals such as the electroencephalogramor speech signals. This paper presents twomethods designed to detect these signal types: the ambiguity filter and the time-frequency correlator. Bothmethods are based on different modifications of the time-frequency-matched filter and bothmethods attempt to overcome the problem of predefining the template set for the matched filter. The ambiguity filter method reduces the number of required templates by one half; the time-frequency correlator method does not require a predefined template set at all. To evaluate their detection performance, we test themethods using simulated and real data sets. Experiential results showthat the two proposed methods, relative to the time-frequency-matched filter, can more accurately detect speech signals and other simulated signals in the presence of colouredGaussian noise. Results also showthat all time-frequency methods outperformthe classical time-domain- matched filter for both simulated and real signals, thus demonstrating the utility of the time-frequency detection approach.