Browsing by Department "Computer Science"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Open Access
    Fast online predictive compression of radio astronomy data
    (2013) Hugo, Benjamin
    This report investigates the fast, lossless compression of 32-bit single precision floating-point values. High speed compression is critical in the context of the MeerKAT radio telescope currently under construction in Southern Africa and Australia, which will produce data at rates up to 1 Petabyte every 20 seconds. The compression technique being investigated is based on predictive compression, which has proven successful at achieving high-speed compression in previous research. Several different predictive techniques (which includes polynomial extrapolation), along with CPU- and GPU-based parallelization approaches are discussed. The implementation successfully achieves throughput rates in excess of 6 GiB/s for compression and much higher rates for decompression using a 64-core AMD Opteron machine, achieving file-size reductions of, on average 9%. Furthermore the results of concurrent investigations into block-based parallel Huffman encoding and Zero-length Encoding are compared to the predictive scheme and it was found that the predictive scheme obtains approximately 4%-5% better compression ratios than the Zero-Length Encoder and is 25 times faster than Huffman encoding on an Intel Xeon E5 processor. The scheme may be well-suited to address the large network bandwidth requirements of the MeerKAT project.
  • Thumbnail Image
    Item
    Open Access
    Improving Searchability of Automatically Transcribed Lectures Through Dynamic Language Modelling
    (2012) Marquard, Stephen
    Recording university lectures through lecture capture systems is increasingly common. However, a single continuous audio recording is often unhelpful for users, who may wish to navigate quickly to a particular part of a lecture, or locate a specific lecture within a set of recordings. A transcript of the recording can enable faster navigation and searching. Automatic speech recognition (ASR) technologies may be used to create automated transcripts, to avoid the significant time and cost involved in manual transcription. Low accuracy of ASR-generated transcripts may however limit their usefulness. In particular, ASR systems optimized for general speech recognition may not recognize the many technical or discipline-specific words occurring in university lectures. To improve the usefulness of ASR transcripts for the purposes of information retrieval (search) and navigating within recordings, the lexicon and language model used by the ASR engine may be dynamically adapted for the topic of each lecture. A prototype is presented which uses the English Wikipedia as a semantically dense, large language corpus to generate a custom lexicon and language model for each lecture from a small set of keywords. Two strategies for extracting a topic-specific subset of Wikipedia articles are investigated: a naïve crawler which follows all article links from a set of seed articles produced by a Wikipedia search from the initial keywords, and a refinement which follows only links to articles sufficiently similar to the parent article. Pair-wise article similarity is computed from a pre-computed vector space model of Wikipedia article term scores generated using latent semantic indexing. The CMU Sphinx4 ASR engine is used to generate transcripts from thirteen recorded lectures from Open Yale Courses, using the English HUB4 language model as a reference and the two topic-specific language models generated for each lecture from Wikipedia.