Detection of sinusoidal signals in noise by probabilistic modelling of the spectral magnitude shape and phase continuity

This paper presents a method for detection of sinusoidal signals corrupted by an additive noise in the short-time Fourier domain. The proposed method is based on probabilistic modelling of the spectral magnitude shape and phase continuity around spectral peaks and can deal with both stationary and non-stationary sinusoidal signals. Experimental results are presented for both sinusoidal signals of a constant frequency and frequency varying continuously over time. The performance is analysed in terms of the false acceptance and false rejection error rates of spectral peak detection and also compared to our previous method. Experimental results demonstrate very high detection accuracy in even very strong noisy conditions.