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  <titleInfo>
    <title>Implementation of low delay, low bit-rate audio compression for wireless digital audio system</title>
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  <name type="personal">
    <namePart>Jessada Karnjana</namePart>
    <role>
      <roleTerm authority="marcrelator" type="text">creator</roleTerm>
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  <name type="personal">
    <namePart>Afzulpurkar, Nitin V.</namePart>
    <role>
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  <name type="personal">
    <namePart>Pasin Israsena</namePart>
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      <roleTerm type="text">Co-chairperson</roleTerm>
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  <name type="personal">
    <namePart>Chumnarn Punyasai</namePart>
    <role>
      <roleTerm type="text">Examination committee</roleTerm>
    </role>
  </name>
  <name type="corporate">
    <namePart>Thailand Graduate Institute of Science and Technology</namePart>
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  <genre authority="marc">technical report</genre>
  <originInfo>
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    <place>
      <placeTerm type="text">Pathum Thani, Thailand</placeTerm>
    </place>
    <publisher>Asian Institute of Technology</publisher>
    <dateIssued>2007</dateIssued>
    <dateIssued encoding="marc">2006</dateIssued>
    <issuance>continuing</issuance>
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    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
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  <physicalDescription>
    <extent>100 p. : ill.</extent>
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  <abstract>The question to increase the compression rate while the reproduction quality to be still acceptable leads to the term of perceptual audio compression. Audio perceptual coding based on a psychoacoustic model which using to eliminate the irrelevant information. Psychoacoustic model is a mathematical model of human perceptual of sound. The goal of model is to develop functions that are related to hearing sensations of human auditory system. For real-time applications, e.g. wireless speaker, headphones, or internet telephony, these applications do not require high audio quality but require low coding delay. To achieve this fashion, I designed a system with low complexity to reduce the computational times but the quality does not much different from audio CD. I used MDCT filter banks as subband coding. The advantage of MDCT is its perfected reconstruction. For quantizing. I compare the coding performance both full-range and narrow-range. A full-range quantization gave poor dequantized signals compare to narrow-range quantization. But full-range is acceptable for a low bit-rate requirement. Adding Huffman coding after quantization improves compression ratio less than 5%. It depends on the nature of data to compress. Huffman coding is useless in the case of less identical data. Thus I designed the system to check the identical data before perform Huffman coding.  This codec gave the average compression ratio about 2.71: I or 62.5%. The number of bits per sample reduced to 5.99 bits per sample b'om 16 hits per sample</abstract>
  <note>A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Microelectronics, School of Engineering and Technology</note>
  <note>Thesis (M.Eng.) - Asian Institute of Technology, 2006</note>
  <subject authority="lcsh">
    <topic>Psychoacoustics</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Auditory perception</topic>
  </subject>
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      <title>Thesis ; no. ME-06-07</title>
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