@InProceedings{8081519,
  author = {D. Sharma and U. Jost and P. A. Naylor},
  booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
  title = {Non-intrusive bit-rate detection of coded speech},
  year = {2017},
  pages = {1799-1803},
  abstract = {We present a non-intrusive codec type and bit-rate detection algorithm that extracts a number of features from a decoded speech signal and models their statistics using a Deep Neural Network (DNN) classifier. We also present a method for reducing the computational complexity and improving the robustness of the algorithm by pruning features that have a low importance and high computational cost using a CART binary tree. The proposed method is tested on a database that includes additive noise and transcoding as well as a real voicemail database. We show that the proposed method has 25% lower complexity than the baseline, 19% higher accuracy in the bitrate detection task and 10% higher accuracy in the CODEC classification experiment.},
  keywords = {computational complexity;feature extraction;neural nets;speech coding;speech recognition;bitrate detection task;nonintrusive bit-rate detection;coded speech;bit-rate detection algorithm;decoded speech signal;computational complexity;deep neural network classifier;CART binary tree;additive noise;transcoding;voicemail database;CODEC classification experiment;Codecs;Speech;Signal processing algorithms;Feature extraction;Databases;Speech coding;Training;CODEC-Identification;Deep-Neural-Network;Bit-Rate;Voicemail-Classifcation;Speech-Quality},
  doi = {10.23919/EUSIPCO.2017.8081519},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570341301.pdf},
}

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