• English
  • Deutsch
  • Log In
    Password Login
    Research Outputs
    Fundings & Projects
    Researchers
    Institutes
    Statistics
Repository logo
Fraunhofer-Gesellschaft
  1. Home
  2. Fraunhofer-Gesellschaft
  3. Konferenzschrift
  4. AAC encoding detection and bitrate estimation using a convolutional neural network
 
  • Details
  • Full
Options
2016
Conference Paper
Title

AAC encoding detection and bitrate estimation using a convolutional neural network

Abstract
In this paper, we propose a new method for AAC encoding detection and bitrate estimation from PCM material. The algorithm is based on a Convolutional Neural Network that can distinguish between eight different bitrates. It achieves an average accuracy of 94.65% by analysis of only 116.10 ms of content.
Author(s)
Seichter, Daniel
Cuccovillo, Luca  
Aichroth, Patrick  
Mainwork
IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016. Proceedings  
Conference
International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2016  
DOI
10.1109/ICASSP.2016.7472041
Language
English
Fraunhofer-Institut für Digitale Medientechnologie IDMT  
Keyword(s)
  • audio forensics

  • neural networks

  • quality assessment

  • MPEG-2 AAC

  • deep learning

  • media forensics

  • Cookie settings
  • Imprint
  • Privacy policy
  • Api
  • Contact
© 2024