This work focuses on robust speech recognition in air traffic control (ATC) by designing a novel processing paradigm to integrate multilingual speech recognition into a single framework using three cascaded modules: an acoustic model (AM), a pronunciation model (PM), and a language model (LM). The AM converts ATC speech into phoneme-based text sequences that the PM then translates into a word-based sequence, which is the ultimate goal of this research. The LM corrects both phoneme- and word-based errors in the decoding results. The AM, including the convolutional neural network (CNN) and recurrent neural network (RNN), considers the spatial and temporal dependences of the speech features and is trained by the connectionist temporal classification loss. To cope with radio transmission noise and diversity among speakers, a multiscale CNN architecture is proposed to fit the diverse data distributions and improve the performance. Phoneme-to-word translation is addressed via a proposed machine translation PM with an encoder-decoder architecture. RNN-based LMs are trained to consider the code-switching specificity of the ATC speech by building dependences with common words. We validate the proposed approach using large amounts of real Chinese and English ATC recordings and achieve a 3.95% label error rate on Chinese characters and English words, outperforming other popular approaches. The decoding efficiency is also comparable to that of the end-to-end model, and its generalizability is validated on several open corpora, making it suitable for real-time approaches to further support ATC applications, such as ATC prediction and safety checking.

Multispectral object detection has attracted increasing attention recently due to its superior detection capacity under various illumination conditions. The key challenge lies in the effective aggregation of multi-spectral features to derive highly discriminative representations. To address this challenge, we propose a novel Multidimensional Fusion Network (MMFN) to explore multi-modal information from local, global, and channel perspectives. Specifically, at the local level, local features of different modalities and their inter-relationships are captured by a window-shifted fusion. As a complement to the local information, we designed a global interaction module that facilitates the fusion of holistic, high-level semantic information spanning the entire image. We distillate the channel dependencies and complementarities between different modalities through cross-channel learning and generate the final fused representation. Comprehensive experiments conducted on three publicly available datasets provide compelling evidence validating the superiority of the proposed methodology. The results exhibit notable performance gains over state-of-the-art multispectral object detectors. Our code will be released.

In most optical fiber distributed acoustic sensing (DAS) systems, to obtain the desired outcome, the sensing signal acquired by DAS systems normally needs to be denoised. In some applications, such as the identifiation of the position of fast-moving targets, we need DAS systems to respond with sufficient speed. However, most classical denoising algorithms do not work if the signals are insufficiently collected within a short period (called a short-time signal). To obtain ideal results within a short time window, we propose an attention-based convolutional neural network (CNN) structure with extremely short signal windows to learn and approximate the results of classical denoising methods. To evaluate the effectiveness of the proposed method, the experiment is conducted under a real field highway scenario where the desired signals are overwhelmed with noise. The results show that by using signals collected within extremely short time windows of 100 ms, an insufficient time for the processing of existing denoising algorithms, our structure yields a satisfactory denoising performance.

The combination of Raman spectroscopy and deep learning technology provides an automatic, rapid, and accurate scheme for the clinical diagnosis of pathogenic bacteria. However, the accuracy of existing deep learning methods is still limited because of the single and fixed scales of deep neural networks. We propose a deep neural network that can learn multi-scale features of Raman spectra by using the automatic combination of multi-receptive fields of convolutional layers. This model is based on the expert knowledge that the discrimination information of Raman spectra is composed of multi-scale spectral peaks. We enhance the interpretability of the model by visualizing the activated wavenumbers of the bacterial spectrum that can be used for reference in related work. Compared with existing state-of-the-art methods, the proposed method achieves higher accuracy and efficiency for bacterial identification on isolate-level, empiric-treatment-level, and antibiotic-resistance-level tasks. The clinical bacterial identification task requires significantly fewer patient samples to achieve similar accuracy. Therefore, this method has tremendous potential for the identification of clinical pathogenic bacteria, antibiotic susceptibility testing, and prescription guidance.

Accurate recognition of named entities from spoken instructions remains a significant challenge for automatic speech recognition (ASR) techniques in air traffic control (ATC), which limits the reliability of ASR-based applications. A promising solution to overcome this challenge is to integrate prior contextual knowledge into ASR since it contains rich named entities used in ATC communications. Although existing studies have investigated ATC-related contextual ASR techniques, there is a lack of benchmarks to evaluate the advantages of different approaches. In this article, a comprehensive comparative study is presented to explore effective contextual ASR approaches for the ATC domain. Specifically, several typical contextual ASR approaches are introduced in ATC to conduct a comprehensive comparison. Moreover, a novel contextual ASR model, denoted CATCNet, is presented to dedicatedly address the domain-specific problems in ATC, such as limited resources, fast speech, and volatile noise. Several evaluation metrics are proposed to validate the performance of comparison approaches based on the practical requirements of ATC efforts. Extensive experiments are conducted across two real-world ATC speech corpora to build the benchmark. The experimental results demonstrated that integrating context knowledge is effective in improving the recognition performance of named entities. Crucially, the proposed CATCNet outperforms other baseline models by confirming all technical improvements, achieving 80.0% and 86.54% instruction recognition accuracy (IRA) on the ATCSpeech and C-ATCSpeech corpora, respectively. It is believed that this work not only overcomes the bottleneck of ASR performance in the ATC domain, but also provides an applicable solution for ATC-related ASR applications.

In this work, a deep learning-based framework is proposed to implement an autonomous pilot agent (APA), which serves as a human pseudo-pilot to assist air traffic controller (ATCO) training. A novel paradigm, including speech recognition, language understanding, pilot repetition generation (PRG), and text-to-speech (TTS), is designed to formulate the framework pipeline, which also incorporates a simulation system interface. We mainly focus on the PRG and TTS models to address the ATC specificities in this work. The neural architecture is proposed to generate the text repetition instruction by using a sequence-to-sequence text mapping. The Transformer block is improved to implement a high-efficient TTS model, in which the nonautoregressive mechanism is applied to achieve the parallel synthesis. A dedicated phoneme vocabulary is designed to cope with the multilingual issue in the ATC domain and address the out-of-vocabulary problem. With the APA framework, a virtual training mode is proposed to complete the training task without the limitation of time and location. Experimental results on a real-world dataset show that the proposed APA framework replaces the human pilot with considerable high confidence in a real-time manner during the simulation training. Most importantly, the APA framework and the virtual training system are able to cope with the dilemma of physical attendance (like COVID-19) and improve the equipment utilization capacity for the ATCO training.