No abstract

This paper introduces DREGON, a novel publiclyavailable dataset that aims at pushing research in sound source localization using a microphone array embedded in an unmanned aerial vehicle (UAV). The dataset contains both clean and noisy in-flight audio recordings continuously annotated with the 3D position of the target sound source using an accurate motion capture system. In addition, various signals of interests are available such as the rotational speed of individual rotors and inertial measurements at all time. Besides introducing the dataset, this paper sheds light on the specific properties, challenges and opportunities brought by the emerging task of UAV-embedded sound source localization. Several baseline methods are evaluated and compared on the dataset, with real-time applicability in mind. Very promising results are obtained for the localization of a broad-band source in loud noise conditions, while speech localization remains a challenge under extreme noise levels.

In the case of a trlgr~m language model, the probability of the next word conditioned on the previous two words is estimated from a large corpus of text. The resulting static trigram language model (STLM) has fixed probabilities that are independent of the document being dictated. To improve the language mode] (LM), one can adapt the probabilities of the trigram language model to match the current document more closely. The partially dictated document provides significant clues about what words ~re more likely to be used next. Of many methods that can be used to adapt the LM, we describe in this paper a simple model based on the trigram frequencies estimated from the partially dictated document. We call this model ~ cache trigram language model (CTLM) since we are c~chlng the recent history of words. We have found that the CTLM red,aces the perplexity of a dictated document by 23%. The error rate of a 20,000-word isolated word recognizer decreases by about 5% at the beginning of a document and by about 24% after a few hundred words.

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NusA and NusG are major regulators of bacterial transcription elongation, which act either in concert or antagonistically. Both bind to RNA polymerase (RNAP), regulating pausing as well as intrinsic and Rho-dependent termination. Here, we demonstrate by nuclear magnetic resonance spectroscopy that the Escherichia coli NusG amino-terminal domain forms a complex with the acidic repeat domain 2 (AR2) of NusA. The interaction surface of either transcription factor overlaps with the respective binding site for RNAP. We show that NusA-AR2 is able to remove NusG from RNAP. Our in vivo and in vitro results suggest that interaction between NusA and NusG could play various regulatory roles during transcription, including recruitment of NusG to RNAP, resynchronization of transcription:translation coupling, and modulation of termination efficiency.