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dc.contributor.authorFazal-E -Wahab
dc.contributor.authorYe, Zhongfu
dc.contributor.authorSaleem, Nasir
dc.contributor.authorAli, Hamza
dc.date2023-05
dc.date.accessioned2023-06-01T10:22:12Z
dc.date.available2023-06-01T10:22:12Z
dc.identifier.issn1989-1660
dc.identifier.urihttps://reunir.unir.net/handle/123456789/14813
dc.description.abstractDeep learning (DL) networks have grown into powerful alternatives for speech enhancement and have achieved excellent results by improving speech quality, intelligibility, and background noise suppression. Due to high computational load, most of the DL models for speech enhancement are difficult to implement for realtime processing. It is challenging to formulate resource efficient and compact networks. In order to address this problem, we propose a resource efficient convolutional recurrent network to learn the complex ratio mask for real-time speech enhancement. Convolutional encoder-decoder and gated recurrent units (GRUs) are integrated into the Convolutional recurrent network architecture, thereby formulating a causal system appropriate for real-time speech processing. Parallel GRU grouping and efficient skipped connection techniques are engaged to achieve a compact network. In the proposed network, the causal encoder-decoder is composed of five convolutional (Conv2D) and deconvolutional (Deconv2D) layers. Leaky linear rectified unit (ReLU) is applied to all layers apart from the output layer where softplus activation to confine the network output to positive is utilized. Furthermore, batch normalization is adopted after every convolution (or deconvolution) and prior to activation. In the proposed network, different noise types and speakers can be used in training and testing. With the LibriSpeech dataset, the experiments show that the proposed real-time approach leads to improved objective perceptual quality and intelligibility with much fewer trainable parameters than existing LSTM and GRU models. The proposed model obtained an average of 83.53% STOI scores and 2.52 PESQ scores, respectively. The quality and intelligibility are improved by 31.61% and 17.18% respectively over noisy speech.es_ES
dc.language.isoenges_ES
dc.publisherInternational Journal of Interactive Multimedia and Artificial Intelligencees_ES
dc.relation.ispartofseries;In Press
dc.relation.urihttps://www.ijimai.org/journal/bibcite/reference/3324es_ES
dc.rightsopenAccesses_ES
dc.subjectConvolutional Gated Recurrent Unit (Convolutional GRU)es_ES
dc.subjectdeep learninges_ES
dc.subjectintelligibilityes_ES
dc.subjectLong Short Term Memory (LSTM)es_ES
dc.subjectspeech enhancementes_ES
dc.subjectIJIMAIes_ES
dc.titleEfficient Gated Convolutional Recurrent Neural Networks for Real-Time Speech Enhancementes_ES
dc.typearticlees_ES
reunir.tag~IJIMAIes_ES
dc.identifier.doihttps://doi.org/10.9781/ijimai.2023.05.007


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