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We propose a non-adaptive unequal error protection (UEP) querying policy based on superposition coding for the noisy 20 questions problem.
In this problem, a player wishes to successively refine an estimate of the value of a continuous random variable by posing binary queries and receiving noisy responses.
When the queries are designed non-adaptively as a single block and the noisy responses are modeled as the outputs of a binary symmetric channel the 20 questions problem can be mapped to an equivalent problem of channel coding with UEP.

In query expansion for object retrieval, there is substantial danger of query drift, where irrelevant information is inferred from pseudo-relevant images to enrich the query. To address this issue, we propose a query expansion method from the viewpoint of diffusion. It explores the structure of highly ranked images in a topological space, assuming that false positives reside on different manifolds from the query. For this purpose, a mutual rank graph is defined on pseudo-relevant images, and their distribution is learned by diffusing their query similarities through the graph.

This paper considers the target localization problem using the hybrid bistatic range and time difference of arrival (TDOA) measurements in multistatic radar. An algebraic closed-form solution to this nonlinear estimation problem is developed through two-stage processing, where the nuisance variables are introduced in the first stage and the localization error of first stage solution is estimated to improve the final target position estimate in the second stage.

Speaking style plays an important role in the expressivity of speech for communication. Hence speaking style is very important for synthetic speech as well. Speaking style adaptation faces the difficulty that the data of specific styles may be limited and difficult to obtain in large amounts. A possible solution is to leverage data from speaking styles that are more available, to train the speech synthesizer and then adapt it to the target style for which the data is scarce.

Bidirectional long short term memory (BLSTM) recurrent neural networks (RNNs) have recently outperformed other state-of-the-art approaches, such as i-vector and deep neural networks (DNNs) in automatic language identification (LID), particularly when testing with very short utterances (∼3s). Mismatches conditions between training and test data, e.g. speaker, channel, duration and environmental noise, are a major source of performance degradation for LID.

Deep learning based approaches have achieved promising performance in speaker-dependent single-channel multi-speaker speech separation.However, partly due to the label permutation problem, they may encounter difficulties in speaker-independent conditions. Recent methods address this problem by some assignment operations. Different from them, we propose a novel source-aware context network, which explicitly inputs speech sources as well as mixture signal.

One major issue of implementing broadband active noise control systems in reverberant
rooms is the lack of reference signals. In this work, by exploiting the spatial sound
field characteristics, a time-domain sound field separation method is developed to
generate the reference signal for broadband active noise control systems in reverberant
rooms. The time-domain sound field separation method separates the outgoing field produced
by the primary source from the secondary source feedback and room reverberation on a