02593nas a2200265   4500000000100000000000100001008004100002260001200043653005900055653001800114653002000132653003400152653002300186100001900209700001500228700001700243700001400260700001300274245009300287856008100380300000800461490001300469520183100482022001402313       9998                            d  c05/202310aConvolutional Gated Recurrent Unit (Convolutional GRU)10aDeep Learning10aIntelligibility10aLong Short Term Memory (LSTM)10aSpeech Enhancement1 aFazal-E -Wahab1 aZhongfu Ye1 aNasir Saleem1 aHamza Ali1 aImad Ali00aEfficient Gated Convolutional Recurrent Neural Networks for Real-Time Speech Enhancement  uhttps://www.ijimai.org/journal/sites/default/files/2023-07/ip2023_05_007.pdf  a1-90 vIn Press3 aDeep 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.  a1989-1660