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Evaluating instrumental measures of speech quality using Bayesian model selection: Correlations can be misleading!

, , and . ICASSP, page 634-638. IEEE, (2016)

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A Simple Cepstral Domain DNN Approach to Artificial Speech Bandwidth Extension., , and . ICASSP, page 5469-5473. IEEE, (2018)An Instrumental Quality Measure for Artificially Bandwidth-Extended Speech Signals., , , , and . IEEE ACM Trans. Audio Speech Lang. Process., 25 (2): 384-396 (2017)Artificial bandwidth extension using deep neural networks for spectral envelope estimation., , and . IWAENC, page 1-5. IEEE, (2016)Sinusoidal-Based Lowband Synthesis for Artificial Speech Bandwidth Extension., and . IEEE ACM Trans. Audio Speech Lang. Process., 27 (4): 765-776 (2019)A subjective listening test of six different artificial bandwidth extension approaches in English, Chinese, German, and Korean., , , , , , , , , and 8 other author(s). ICASSP, page 5915-5919. IEEE, (2016)Objective Assessment of Artificial Speech Bandwidth Extension Approaches., , , , and . ITG Symposium on Speech Communication, page 1-5. IEEE, (2016)Simulation and Practice: A Hybrid Experimentation Platform for TSN., , , , , , and . IFIP Networking, page 1-9. IEEE, (2023)DNN-based artificial bandwidth extension - enhancement and instrumental assessment of speech quality.. Braunschweig University of Technology, Germany, (2021)Artificial Speech Bandwidth Extension Using Deep Neural Networks for Wideband Spectral Envelope Estimation., and . IEEE ACM Trans. Audio Speech Lang. Process., 26 (1): 71-83 (2018)A DNN regression approach to speech enhancement by artificial bandwidth extension., and . WASPAA, page 219-223. IEEE, (2017)