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Using recurrences in time and frequency within U-net architecture for speech enhancement

Citation Author(s):
Szymon Drgas
Submitted by:
Tomasz Grzywalski
Last updated:
8 May 2019 - 9:13am
Document Type:
Poster
Document Year:
2019
Event:
Presenters Name:
Tomasz Grzywalski
Paper Code:
3235

Abstract 

Abstract: 

When designing fully-convolutional neural network, there is a trade-off between receptive field size, number of parameters and spatial resolution of features in deeper layers of the network. In this work we present a novel network design based on combination of many convolutional and recurrent layers that solves these dilemmas. We compare our solution with U-nets based models known from the literature and other baseline models on speech enhancement task. We test our solution on TIMIT speech utterances combined with noise segments extracted from NOISEX-92 database and show clear advantage of proposed solution in terms of SDR (signal-to-distortion ratio), SIR (signal-to-interference ratio) and STOI (spectro-temporal objective intelligibility) metrics compared to the current state-of-the-art.

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