Improved baseline for REVERB challenge 
  ======================================
  
  This is an improvement over "Improved multi condition training baseline" from Felix Weninger & Shinji Watanabe
  
  Key specs:
  - Nara-WPE and BeamformIt front-end enhancement
  - TDNN acoustic model
  
  RESULT:
  For experiment results, please see RESULTS for more detail
  
  REFERENCE:
  ++++++++
  If you find this software useful for your own research, please cite the
  following papers:
  
  Felix Weninger, Shinji Watanabe, Jonathan Le Roux, John R. Hershey, Yuuki
  Tachioka, Jürgen Geiger, Björn Schuller, Gerhard Rigoll: "The MERL/MELCO/TUM
  system for the REVERB Challenge using Deep Recurrent Neural Network Feature
  Enhancement", Proc. REVERB Workshop, IEEE, Florence, Italy, May 2014.
  
  Lukas Drude, Jahn Heymann, Christoph Boeddeker, and Reinhold Haeb-Umbach:
  "NARA-WPE: A Python package for weighted prediction error dereverberation in
  Numpy and Tensorflow for online and offline processing." In Speech Communication;
  13th ITG-Symposium, pp. 1-5. VDE, 2018.
  
  INSTRUCTIONS:
  +++++++++++++
  1) Execute the training and recognition steps by
  
     ./run.sh
  
     Depending on your system specs (# of CPUs, RAM) you might want (or have) to 
     change the number of parallel jobs -- this is controlled by the nj
     and decode_nj variables (# of jobs for training, for decoding).