A Sound Engineering Approach to Near End Listening Enhancement

Carol Chermaz, Simon King


We present the beta version of ASE (the Automatic Sound Engineer), a NELE (Near End Listening Enhancement) algorithm based on audio engineering knowledge. Generations of sound engineers have improved the intelligibility of speech against competing sounds and reverberation, while maintaining high sound quality and artistic integrity (e.g., audio track mixing in music and movies). We try to grasp the essential aspects of this expert knowledge and apply it to the more mundane context of speech playback in realistic noise. The algorithm described here was entered into the Hurricane Challenge 2.0, an evaluation of NELE algorithms. Results from those listening tests across three languages show the potential of our approach, which achieved improvements of over 7 dB EIC (Equivalent Intensity Change), corresponding to an absolute increase of 58% WAR (Word Accuracy Rate).


 DOI: 10.21437/Interspeech.2020-2748

Cite as: Chermaz, C., King, S. (2020) A Sound Engineering Approach to Near End Listening Enhancement. Proc. Interspeech 2020, 1356-1360, DOI: 10.21437/Interspeech.2020-2748.


@inproceedings{Chermaz2020,
  author={Carol Chermaz and Simon King},
  title={{A Sound Engineering Approach to Near End Listening Enhancement}},
  year=2020,
  booktitle={Proc. Interspeech 2020},
  pages={1356--1360},
  doi={10.21437/Interspeech.2020-2748},
  url={http://dx.doi.org/10.21437/Interspeech.2020-2748}
}