The dual-path RNN (DPRNN) was proposed to more effectively model extremely long
sequences for speech separation in the time domain. By splitting long sequences to smaller …

In the field of speech enhancement, time domain methods have difficulties in achieving both
high performance and efficiency. Recently, dual-path models have been adopted to …

In this work, we propose a new model called triple-path attentive recurrent network (TPARN)
for multichannel speech enhancement in the time domain. TPARN extends a single-channel …

Speech enhancement (SE) reduces background noise signals in target speech and is
applied at the front end in various real-world applications, including robust ASRs and real …

Deep neural networks (DNNs) represent the mainstream methodology for supervised
speech enhancement, primarily due to their capability to model complex functions using …

Speech enhancement (SE) aims to improve the intelligibility and perceptual quality of
speech contaminated by noise signals through spectral or temporal changes. Deep learning …

Deep learning based speech enhancement has achieved remarkable success, but
challenges remain in low signal-to-noise ratio (SNR) nonstationary noise scenarios. In this …

Recent deep learning models have achieved high performance in speech enhancement;
however, it is still challenging to obtain a fast and low-complexity model without significant …

One of the key indicators for evaluating finished cement products is the cement specific
surface area. The soft sensor model for the cement specific surface area serves as the …

Nowadays, in the field of data mining, time series data analysis is a very important and
challenging subject. This is especially true for time series remote sensing classification. The …