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Deep unfolding has recently been proposed to derive novel deep network architectures from model-based approaches. In this paper, we consider its application to multichannel source separation. We unfold a multichannel Gaussian mixture model (MCGMM), resulting in a deep MCGMM computational network that directly processes complex-valued frequency-domain multichannel audio and has an architecture defined explicitly by a generative model, thus combining the advantages of deep networks and model-based approaches.

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This work proposes two methods for providing speech privacy between spatial zones in anechoic and reverberant environments. The methods are based on masking the content leaked between regions. The masking is optimised to maximise the speech intelligibility contrast (SIC) between the zones. The first method uses a uniform masker signal that is combined with desired multizone loudspeaker signals and requires acoustic contrast between zones.

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This paper describes the generation of a morphable model for external ear shapes. The aim for the morphable model is to characterize an ear shape using only a few parameters in order to assist the study of morphoacoustics. The model is derived from a statistical analysis of a population of 58 ears from the SYMARE database. It is based upon the framework of large deformation diffeomorphic metric mapping (LDDMM) and the vector space that is constructed over the space of initial momentums describing the diffeomorphic transformations.

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Spatial audio reproduction is essential to create a natural listening experience for digital media. Majority of the legacy audio contents are in channel-based format, which is very particular on the desired playback system. Considering the diversity of today’s playback systems, the quality of reproduced sound scenes degrades significantly when mismatches between the audio content and the playback system occur.

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Spatial audio reproduction is essential to create a natural listening experience for digital media. Majority of the legacy audio contents are in channel-based format, which is very particular on the desired playback system. Considering the diversity of today’s playback systems, the quality of reproduced sound scenes degrades significantly when mismatches between the audio content and the playback system occur.

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10 Views

Head related transfer function (HRTF) is widely used in 3D audio reproduction, especially over headphones. Conventionally, HRTF database is acquired at discrete directions and the acquisition process is time-consuming. Recent works have been proposed to improve HRTF acquisition efficiency via continuous acquisition. However, these HRTF acquisition techniques still require subject to sit still (with limited head movement) in a rotating chair. In this paper, we further relax the head movement constraint during acquisition by using a head tracker.

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With the strong growth of the mobile devices and emerging virtual reality (VR) and augmented reality (AR) applications, headsets are becoming more and more preferable in personal listening due to its convenience and portability. Assistive listening (AL) devices like hearing aids have seen much advancement. Creating a natural and authentic listening experience is the common objective of these VR, AR, and AL applications. In this tutorial, we will present state-of-the-art audio and acoustic signal processing techniques to enhance the sound reproduction in headsets and hearing aids.

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