Deep Residual-Dense Lattice Network for Speech Enhancement

Convolutional neural networks (CNNs) with residual links (ResNets) and causal dilated convolutional units have been the network of choice for deep learning approaches to speech enhancement. While residual links improve gradient flow during training, feature diminution of shallow layer outputs can occur due to repetitive summations with deeper layer outputs. One strategy to improve feature re-usage is to fuse both ResNets and densely connected CNNs (DenseNets). DenseNets, however, over-allocate parameters for feature re-usage. Motivated by this, we propose the residual-dense lattice network (RDL-Net), which is a new CNN for speech enhancement that employs both residual and dense aggregations without over-allocating parameters for feature re-usage. This is managed through the topology of the RDL blocks, which limit the number of outputs used for dense aggregations. Our extensive experimental investigation shows that RDL-Nets are able to achieve a higher speech enhancement performance than CNNs that employ residual and/or dense aggregations. RDL-Nets also use substantially fewer parameters and have a lower computational requirement. Furthermore, we demonstrate that RDL-Nets outperform many state-of-the-art deep learning approaches to speech enhancement.Comment: 8 pages, Accepted by AAAI-202

Machine learning in Magnetic Resonance Imaging: Image reconstruction.

Magnetic Resonance Imaging (MRI) plays a vital role in diagnosis, management and monitoring of many diseases. However, it is an inherently slow imaging technique. Over the last 20 years, parallel imaging, temporal encoding and compressed sensing have enabled substantial speed-ups in the acquisition of MRI data, by accurately recovering missing lines of k-space data. However, clinical uptake of vastly accelerated acquisitions has been limited, in particular in compressed sensing, due to the time-consuming nature of the reconstructions and unnatural looking images. Following the success of machine learning in a wide range of imaging tasks, there has been a recent explosion in the use of machine learning in the field of MRI image reconstruction. A wide range of approaches have been proposed, which can be applied in k-space and/or image-space. Promising results have been demonstrated from a range of methods, enabling natural looking images and rapid computation. In this review article we summarize the current machine learning approaches used in MRI reconstruction, discuss their drawbacks, clinical applications, and current trends

Traditional Village Classification Model Based on Transformer Network

The study of traditional villages holds significant implications in cultural, historical, and societal contexts. Despite the considerable research focus on the architectural styles of Qiang, Tibetan, Han, and Hui ethnic villages due to their distinctiveness, rapidly and accurately identifying the types of traditional villages in practical surveys remains a challenge. To address this issue, this paper establishes an aerial image dataset for Qiang, Tibetan, Han, and Hui ethnic villages and introduces a specialized feature extraction network, Transformer-Village, designed for the classification and detection of traditional villages using deep learning algorithms. The overall structure of the network is lightweight, incorporating condconv dynamic convolution as the core layer structure; furthermore, a spatial self-attention-related feature extraction network is designed based on Transformer. In conclusion, through simulated experiments, Transformer-Village coupled with the YOLO detector achieves a 97.2% mAP on the test set, demonstrating superior detection accuracy compared to other baseline models. Overall, the experimental results suggest that this work is feasible and practical

NSE-CATNet: deep neural speech enhancement using convolutional attention transformer network

Speech enhancement (SE) is a critical aspect of various speech-processing applications. Recent research in this field focuses on identifying effective ways to capture the long-term contextual dependencies of speech signals to enhance performance. Deep convolutional networks (DCN) using self-attention and the Transformer model have demonstrated competitive results in SE. Transformer models with convolution layers can capture short and long-term temporal sequences by leveraging multi-head self-attention, which allows the model to attend the entire sequence. This study proposes a neural speech enhancement (NSE) using the convolutional encoder-decoder (CED) and convolutional attention Transformer (CAT), named the NSE-CATNet. To effectively process the time-frequency (T-F) distribution of spectral components in speech signals, a T-F attention module is incorporated into the convolutional Transformer model. This module enables the model to explicitly leverage position information and generate a two-dimensional attention map for the time-frequency speech distribution. The performance of the proposed SE is evaluated using objective speech quality and intelligibility metrics on two different datasets, the VoiceBank-DEMAND Corpus and the LibriSpeech dataset. The experimental results indicate that the proposed SE outperformed the competitive baselines in terms of speech enhancement performance at -5dB, 0dB, and 5dB. This suggests that the model is effective at improving the overall quality by 0.704 with VoiceBank-DEMAND and by 0.692 with LibriSpeech. Further, the intelligibility with VoiceBank-DEMAND and LibriSpeech is improved by 11.325% and 11.75% over the noisy speech signals

Multi-attention bottleneck for gated convolutional encoder-decoder-based speech enhancement

Convolutional encoder-decoder (CED) has emerged as a powerful architecture, particularly in speech enhancement (SE), which aims to improve the intelligibility and quality and intelligibility of noise-contaminated speech. This architecture leverages the strength of the convolutional neural networks (CNNs) in capturing high-level features. Usually, the CED architectures use the gated recurrent unit (GRU) or long-short-term memory (LSTM) as a bottleneck to capture temporal dependencies, enabling a SE model to effectively learn the dynamics and long-term temporal dependencies in the speech signal. However, Transformers neural networks with self-attention effectively capture long-term temporal dependencies. This study proposes a multi-attention bottleneck (MAB) comprised of a self-attention Transformer powered by a time-frequency attention (TFA) module followed by a channel attention module (CAM) to focus on the important features. The proposed bottleneck (MAB) is integrated into a CED architecture and named MAB-CED. The MAB-CED uses an encoder-decoder structure including a shared encoder and two decoders, where one decoder is dedicated to spectral masking and the other is used for spectral mapping. Convolutional Gated Linear Units (ConvGLU) and Deconvolutional Gated Linear Units (DeconvGLU) are used to construct the encoder-decoder framework. The outputs of two decoders are coupled by applying coherent averaging to synthesize the enhanced speech signal. The proposed speech enhancement is examined using two databases, VoiceBank+DEMAND and LibriSpeech. The results show that the proposed speech enhancement outperforms the benchmarks in terms of intelligibility and quality at various input SNRs. This indicates the performance of the proposed MAB-CED at improving the average PESQ by 0.55 (22.85%) with VoiceBank+DEMAND and by 0.58 (23.79%) with LibriSpeech. The average STOI is improved by 9.63% (VoiceBank+DEMAND) and 9.78% (LibriSpeech) over the noisy mixtures