rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Advance Compression and Watermarking Technique for Speech Signals
  name:
    name part: Thanki, Rohit.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Borisagar, Komal.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Borra, Surekha.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2018
    edition: 1st ed. 2018.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVIII, 69 p. 38 illus., 28 illus. in color. online resource.
  abstract: This book introduces methods for copyright protection and compression for speech signals. The first method introduces copyright protection of speech signal using watermarking; the second introduces compression of the speech signal using Compressive Sensing (CS). Both methods are tested and analyzed. The speech watermarking method uses technology such as Finite Ridgelet Transform (FRT), Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD). The performance of the method is evaluated and compared with existing watermarking methods. In the speech compression method, the standard Compressive Sensing (CS) process is used for compression of the speech signal. The performance of the proposed method is evaluated using various transform bases like Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Singular Value Decomposition (SVD), and Fast Discrete Curvelet Transform (FDCuT).
  table of contents: Introduction -- Background Information -- Speech Watermarking Technique using Ridgelet, DWT and SVD -- Speech Compression Technique using CS Theory -- Conclusions -- References.
  note: by Rohit Thanki, Komal Borisagar, Surekha Borra.
  subject:
    topic: Signal processing
  subject:
    topic: Image processing
  subject:
    topic: Speech processing systems
  subject:
    topic: Computational linguistics
  subject:
    topic: Natural language processing (Computer science)
  subject:
    topic: Database management
  subject:
    topic: Signal, Image and Speech Processing
  subject:
    topic: Computational Linguistics
  subject:
    topic: Natural Language Processing (NLP)
  subject:
    topic: Database Management
  classification: TK5102.9
  classification: TA1637-1638
  classification: 621.382
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: SpringerBriefs in Speech Technology, Studies in Speech Signal Processing, Natural Language Understanding, and Machine Learning
  identifier: 9783319690698
  identifier: https://doi.org/10.1007/978-3-319-69069-8
  record info:
    record creation date: 171103
    record change date: 20200303123810.0
    record identifier: 978-3-319-69069-8