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Comparison of effects on subjective intelligibility and quality of speech in babble for two algorithms
The effects on speech intelligibility and sound quality of two noise-reduction algorithms were compared: a deep recurrent neural network (RNN) and spectral subtraction (SS). The RNN was trained using sentences spoken by a large...
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Effectiveness of a loudness model for time-varying sounds in equating the loudness of sentences subjected to different forms of signal processing.
A model for the loudness of time-varying sounds [Glasberg and Moore (2012). J. Audio. Eng. Soc. 50, 331-342] was assessed for its ability to predict the loudness of sentences that were processed to either decrease or increase...
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Transient Noise Reduction Using a Deep Recurrent Neural Network
A deep recurrent neural network (RNN) for reducing transient sounds was developed and its effects on subjective speech intelligibility and listening comfort were investigated. The RNN was trained using sentences spoken with...
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Use of a Deep Recurrent Neural Network to Reduce Wind Noise
Despite great advances in hearing-aid technology, users still experience problems with noise in windy environments. The potential benefits of using a deep recurrent neural network (RNN) for reducing wind noise were assessed. The...
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