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The human auditory system is able to focus on one speech signal and ignore other speech signals in an auditory scene where several conversations are taking place. This ability of the human auditory system is referred to as the “cocktail-party effect”. This property of human hearing is partly made possible by binaural listening. Interaural time differences (ITDs) and interaural level differences (ILDs) between the ear input signals are the two most important binaural cues for localization of sound sources, i.e. the estimation of source azimuth angles. This paper proposes an implementation of a cocktail-party processor. The proposed cocktail-party processor carries out an auditory scene analysis by estimating the binaural cues corresponding to the directions of the sources. And next, as a function of these cues, suppresses components of signals arriving from non-desired directions, by speech enhancement techniques. The performance of the proposed algorithm is assessed in terms of directionality and speech quality. The proposed algorithm improves existing cocktail-party processors since it combines low computational complexity and efficient source separation. Moreover the advantage of this cocktailparty processor over conventional beam forming is that it enables a highly directional beam over a wide frequency range by using only two microphones.

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Various methods have been proposed for detecting the onset times of musical notes in audio signals. We examine recent work on onset detection using spectral features such as the magnitude, phase and complex domain representations, and propose improvements to these methods: a weighted phase deviation function and a halfwave rectified complex difference. These new algorithms are compared with several state-of-the-art algorithms from the literature, and these are tested using a standard data set of short excerpts from a range of instruments (1060 onsets), plus a much larger data set of piano music (106054 onsets). Some of the results contradict previously published results and suggest that a similarly high level of performance can be obtained with a magnitude-based (spectral flux), a phase-based (weighted phase deviation) or a complex domain (complex difference) onset detection function.

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