A machine-hearing system exploiting head movements for binaural sound localisation in reverberant conditions

This paper is concerned with machine localisation of multiple active speech sources in reverberant environments using two (binaural) microphones. Such conditions typically present a problem for `classical' binaural models. Inspired by the human ability to utilise head movements, the current study investigated the influence of different head movement strategies on binaural sound localisation. A machine-hearing system that exploits a multi-step head rotation strategy for sound localisation was found to produce the best performance in simulated reverberant acoustic space. This paper also reports the public release of a free binaural room impulse responses (BRIRs) database that allows the simulation of head rotation used in this study

A metric for predicting binaural speech intelligibility in stationary noise and competing speech maskers

One criterion in the design of binaural sound scenes in audio production is the extent to which the intended speech message is correctly understood. Object-based audio broadcasting systems have permitted sound editors to gain more access to the metadata (e.g., intensity and location) of each sound source, providing better control over speech intelligibility. The current study describes and evaluates a binaural distortion-weighted glimpse proportion metric -- BiDWGP -- which is motivated by better-ear glimpsing and binaural masking level differences. BiDWGP predicts intelligibility from two alternative input forms: either binaural recordings or monophonic recordings from each sound source along with their locations. Two listening experiments were performed with stationary noise and competing speech, one in the presence of a single masker, the other with multiple maskers, for a variety of spatial configurations. Overall, BiDWGP with both input forms predicts listener keyword scores with correlations of 0.95 and 0.91 for single- and multi-masker conditions, respectively. When considering masker type separately, correlations rise to 0.95 and above for both types of maskers. Predictions using the two input forms are very similar, suggesting that BiDWGP can be applied to the design of sound scenes where only individual sound sources and their locations are available

Software and data for the paper and presentation: Estimating the Loudness Balance of Musical Mixtures using Audio Source Separation

<p>All code and data used to produce the paper and presentation for the paper:</p> <p>D. Ward, H. Wierstorf, R. D. Mason, M. D. Plumbley, and C. Hummersone, “Estimating the Loudness Balance of Musical Mixtures using Audio Source Separation,” in <em>3rd Workshop on Intelligent Music Production</em>, Salford, UK, 2017.</p> <p>The pdf of the paper can be accessed at</p> <p>http://epubs.surrey.ac.uk/841966/</p> <p>Also, see the Musical Audio Repurposing using Source Separation (MARuSS) website:</p> <p>https://cvssp.github.io/maruss-website/</p> <p>This publication corresponds to revision 7:ff82ab47b8f9 of the code repository which is maintained at:</p> <p>https://code.soundsoftware.ac.uk/hg/wimp17-ward-et-al</p