Modular dynamic deep denoising autoencoder for speech enhancement

Deep Denoising Autoencoder (DDAE) is an effective method for noise reduction and speech enhancement. However, a single DDAE with a fixed number of frames for neural network input cannot extract contextual information sufficiently. It has also less generalization in unknown SNRs (signal-to-noise-rati...

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Veröffentlicht in:	2017 7th International Conference on Computer and Knowledge Engineering (ICCKE) S. 254 - 259
Hauptverfasser:	Safari, Razieh, Ahadi, Seyed Mohammad, Seyedin, Sanaz
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.10.2017
Schlagworte:	Deep Denoising Autoencoder (DDAE) Improved Context Pattern Modular Dynamic Deep Denoising Autoencoder (MD-DDAE) Neural networks Noise measurement Noise reduction Signal to noise ratio Speech Speech enhancement Training Unseen Signal to Noise Ratio
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