Signal processing methods have been developed over the last 60 years to detect and analyse complicated non-stationary signals, such as speech and seismic activity. The traditional method for analysing such signals is through a spectrogram based on the short-time Fourier transform (STFT). However, the STFT is not ideal since it reflects only the stationary properties contained in any short time-...

While Independent Subspace Analysis provides a means of blindly separating sound sources from a single channel signal, it does have a number of problems. In particular the amount of information required for separation of sources varies with the signal. This is as a result of the variance-based nature of Principal Component Analysis, which is used for dimensional reduction in the Independent Sub...

The question of detecting a chirp in the time–frequency plane is addressed. Strategies based on line integration are discussed with respect to optimality and adequacy of a representation to a given chirp. Linear and power-law chirps are considered in some detail and a possible application of affine distributions to the detection of gravitational waves is proposed, together with an effective imp...

We present an enhanced sinusoidal modeling system that efficiently parameterizes spectral peaks by linear frequency chirp rate, in addition to the standard parameters of amplitude, center frequency, and phase. Similar to a conventional sinusoidal modeling systems, the current system operates in a frame-by-frame manner, but also efficiently obtains a chirp parameter estimate for each peak in a g...

This paper is part 5 in a series of papers about the Discrete Fourier Transform (DFT) and the Inverse Discrete Fourier Transform (IDFT). The focus of this paper is on the spectrogram. The spectrogram performs a Short-Time Fourier Transform (STFT) in order to estimate the spectrum of a signal as a function of time. The approach requires that each time segment be transformed into the frequency do...

This short article summarizes UCL’s entry for the PASCAL Classifying Heart Sounds Challenge. The approach focused on the creation of novel segmentation and classification methods based on wavelet decomposition and spectrogram analysis.

Low probability of intercept radar signals, which are often problematic to detect and characterize, have as their goal ‘to see and not be seen’. Digital intercept receivers are currently moving away from Fourier-based analysis and towards classical time-frequency analysis techniques for the purpose of analyzing these low probability of intercept radar signals. This paper presents the novel appr...