There are currently many spatial audio reproduction systems in domestic use (e.g. mono, stereo, surround sound, sound bars, and headphones). In an experiment, pairwise preference magnitude ratings for a range of such systems were collected from trained and untrained listeners. The ratings were analysed using internal preference mapping to: (i) uncover the principal perceptual dimensions of listener preference; (ii) label the dimensions based on important perceptual attributes; and (iii) observe differences between trained and untrained listeners. To aid with labelling the dimensions, perceptual attributes were elicited alongside the preference ratings and were analysed by: (i) considering a metric derived from the frequency of use of each attribute and the magnitude of the related preference judgements; and (ii) observing attribute use for comparisons between specific methods. The first preference dimension accounted for over 90% of the variance in ratings; all participants exhibited a preference for reproduction methods that were positively correlated with the first dimension (most notably 5-, 9-, and 22-channel surround sound). This dimension was related to multiple important attributes, including those associated with spatial capability and absence of distortions. The second dimension accounted for only a very small proportion of the variance, and appeared to separate the headphone method from the other methods. The trained and untrained listeners generally showed opposite preferences in the second dimension, suggesting that trained listeners have a higher preference for headphone reproduction than untrained listeners.

A number of metrics has been proposed in the literature to assess sound source separation algorithms. The addition of convolutional distortion raises further questions about the assessment of source separation algorithms in reverberant conditions as reverberation is shown to undermine the optimality of the ideal binary mask (IBM) in terms of signal-to-noise ratio (SNR). Furthermore, with a range of mixture parameters common across numerous acoustic conditions, SNR-based metrics demonstrate an inconsistency that can only be attributed to the convolutional distortion. This suggests the necessity for an alternate metric in the presence of convolutional distortion, such as reverberation. Consequently, a novel metric-dubbed the IBM ratio (IBMR)-is proposed for assessing source separation algorithms that aim to calculate the IBM. The metric is robust to many of the effects of convolutional distortion on the output of the system and may provide a more representative insight into the performance of a given algorithm .

Reverberation continues to present a major problem for sound source separation algorithms. However, humans demonstrate a remarkable robustness to reverberation and many psychophysical and perceptual mechanisms are well documented. The precedence effect is one of these mechanisms; it aids our ability to localize sounds in reverberation. Despite this, relatively little work has been done on incorporating the precedence effect into automated source separation. Furthermore, no work has been carried out on adapting a precedence model to the acoustic conditions under test and it is unclear whether such adaptation, analogous to the perceptual Clifton effect, is even necessary. Hence, this study tests a previously proposed binaural separation/precedence model in real rooms with a range of reverberant conditions. The precedence model inhibitory time constant and inhibitory gain are varied in each room in order to establish the necessity for adaptation to the acoustic conditions. The paper concludes that adaptation is necessary and can yield significant gains in separation performance. Furthermore, it is shown that the initial time delay gap and the direct-to-reverberant ratio are important factors when considering this adaptation.

An algorithm is described which detects auditory onsets quickly in arbitrary binaural audio streams. Aspects of the precedence effect are implemented to speed up computation, and to increase the usability of the output. The onset detector is tested with a number of binaural signals. Onsets that are suitable for spatial auditory processing are found reliably. This will allow spatial feature extraction to be performed.