Asger Heidemann Andersen

Søren Holdt Jensen

IEEE Access, 2022

2020

End-to-End Speech Intelligibility Prediction Using Time-Domain Fully Convolutional Neural Networks.

[BibT_eX]

[DOI]

Mathias Bach Pedersen

Morten Kolbæk

Søren Holdt Jensen

Proceedings of the Interspeech 2020, 2020

A Neural Network for Monaural Intrusive Speech Intelligibility Prediction.

[BibT_eX]

[DOI]

Mathias Bach Pedersen

Søren Holdt Jensen

Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

2018

Nonintrusive Speech Intelligibility Prediction Using Convolutional Neural Networks.

[BibT_eX]

[DOI]

IEEE ACM Trans. Audio Speech Lang. Process., 2018

Refinement and validation of the binaural short time objective intelligibility measure for spatially diverse conditions.

[BibT_eX]

[DOI]

Speech Commun., 2018

2017

On the Use of Band Importance Weighting in the Short-Time Objective Intelligibility Measure.

[BibT_eX]

[DOI]

Proceedings of the Interspeech 2017, 2017

A non-intrusive Short-Time Objective Intelligibility measure.

[BibT_eX]

[DOI]

Proceedings of the 2017 IEEE International Conference on Acoustics, 2017

2016

Predicting the Intelligibility of Noisy and Nonlinearly Processed Binaural Speech.

[BibT_eX]

[DOI]

IEEE ACM Trans. Audio Speech Lang. Process., 2016

Speech intelligibility prediction as a classification problem.

[BibT_eX]

[DOI]

Esther Schoenmaker

Steven van de Par

Proceedings of the 26th IEEE International Workshop on Machine Learning for Signal Processing, 2016

A method for predicting the intelligibility of noisy and non-linearly enhanced binaural speech.

[BibT_eX]

[DOI]

Proceedings of the 2016 IEEE International Conference on Acoustics, 2016

2015

A binaural short time objective intelligibility measure for noisy and enhanced speech.

[BibT_eX]

[DOI]