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- W4313142532 abstract "The intelligibility of demodulated audio signals from analog high frequency transmissions, e.g., using single-sideband (SSB) modulation, can be severely degraded by channel distortions and/or a mismatch between modulation and demodulation carrier frequency. In this work a neural network (NN)-based approach for carrier frequency offset (CFO) estimation from demodulated SSB signals is proposed, whereby a task specific architecture is presented. Additionally, a simulation framework for SSB signals is introduced and utilized for training the NNs. The CFO estimator is combined with a speech enhancement net-work to investigate its influence on the enhancement performance. The NN-based system is compared to a recently proposed pitch tracking based approach on publicly available data from real high frequency transmissions. Experiments show that the NN exhibits good CFO estimation properties and results in significant improvements in speech intelligibility, especially when combined with a noise reduction network." @default.
- W4313142532 created "2023-01-06" @default.
- W4313142532 creator A5021547071 @default.
- W4313142532 creator A5036255349 @default.
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- W4313142532 date "2022-08-29" @default.
- W4313142532 modified "2023-10-02" @default.
- W4313142532 title "Neural Network Based Carrier Frequency Offset Estimation From Speech Transmitted Over High Frequency Channels" @default.
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- W4313142532 doi "https://doi.org/10.23919/eusipco55093.2022.9909971" @default.
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