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ICASSP is the world's largest and most comprehensive technical conference on signal processing and its applications. It provides a fantastic networking opportunity for like-minded professionals from around the world. ICASSP 2016 conference will feature world-class presentations by internationally renowned speakers and cutting-edge session topics.

Image inpainting consists in filling missing regions of an image by inferring from the surrounding content.
In the case of texture images, inpainting can be formulated in terms of conditional simulation of a stochastic texture model.
Many texture synthesis methods thus have been adapted to texture inpainting, but these methods do not offer theoretical guarantees since the conditional sampling is in general only approximate.

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High performance diarisation is a necessity for a variety of applications, and the task has been
studied extensively in the context of broadcast news and meeting processing. Upon introduction of
the task in NIST led evaluations, diarisation error rate (DER) was introduced as the standard metric
for evaluation, and it has been consistently used to compare systems ever since. DER is a frame
based metric that does not penalise for producing many short segments. However, practical systems

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Towards a better understanding of emotion in speech, it is important to understand how emotion changes and when it changes. Recognizing emotions using pre-segmented speech utterances results in a loss in continuity of emotions and does not provide insights into emotion changes. In this paper, we propose an investigation into emotion change detection from the perspective of exchangeability of data points observed sequentially using a martingale framework. Within the framework, a per-frame GMM likelihood based approach is proposed as a measure of strangeness from a particular emotion class.

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These slides were produced to complement the lecture presentation delivered at ICASSP 2016 for our paper on distributed beamforming. Please find the abstract below.

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Passive bistatic radar (PBR) systems use existing RF broadcast and communication signals in the environment for surveillance and tracking applications. GSM mobile communication signal based PBR systems are suitable for short range surveillance systems, but the low-bandwidth of the signal results in low range resolutions when classical cross-correlation based processing is used for target detection. An alternative and more robust approach based on compressive sensing (CS) is proposed here to achieve high range resolution by performing fine gridding for the target scene.

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