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Echo labeling, the challenging task of assigning acoustic reflections to image sources, is equivalent to the highly-important disambiguation task in room geometry inference. A method using the Radon transform, an image processing tool, is proposed to address this challenge. The method relies on acoustic wavefront detection in room impulse response stacks, obtained with a uniform linear array of loudspeakers and one microphone. We show in our experiments that the proposed method can both label and detect echoes.

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In this paper, we investigate the effects of compensating for wave-domain filtering delay in an active speech control system. An active control system utilising wave-domain processed basis functions is evaluated for a linear array of dipole secondary sources. The target control soundfield is matched in a least squares sense using orthogonal wavefields to a predicted future target soundfield. Filtering is implemented using a block-based short-time signal processing approach which induces an inherent delay.

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In this paper we deal with the estimation of the room impulse response (RIR) between each loudspeaker and each microphone of a wireless acoustic network of two nodes when used to implement a crosstalk canceller. The nodes of the network are commercial devices connected via standard wireless links, presenting low computational requirements and non-ideal synchronization between them. Moreover, the nodes can exchange information, but they cannot share their signals due to the high throughput and perfect synchronism that would be required.

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When using a set of generic head-related transfer functions (HRTFs) for spatial sound rendering, personalisation can be considered to minimise localisation errors. This typically involves tuning the characteristics of the HRTFs or a parametric model according to the listener’s anthropometry. However, measuring anthropometric features directly remains a challenge in practical applications, and the mapping between anthropometric and acoustic features is an open research problem.

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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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