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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Akter, Mst Shapna Shahriar, Hossain Rahman, Md Abdur Rahman, Muhammad Cuzzocrea, Alfredo University of West Florida Department of Intelligent Systems and Robotics United States University of West Florida Center for Cybersecurity United States Jahangirnagar University Department of Mathematics Dhaka Savar Bangladesh Clayton State University Department of Computer Science United States University of Calabria IDEA Lab Rende Italy

ISBN: (纸本)9798350359015

In recent times, the cryptocurrency market has emerged as one of the fastest-growing financial markets worldwide. It is, however, known for its high volatility and illiquidity compared to traditional markets such as equities, foreign exchange, and commodities. This inherent risk creates uncertainty among investors. The aim of this research is to forecast the level of risk in the cryptocurrency market. To assist cryptocurrency investors in navigating these challenges, we propose an approach that involves calculating the risk factor based on existing parameters. We employed various machine learning algorithms, including CNN, LSTM, BiLSTM, and GRU, to predict the risk factor in twenty elements of the cryptocurrency market. Through extensive experimentation, we developed a new model that outperformed existing models, achieving the highest Root Mean Square Error (RMSE) value of 1.3229 and the lowest RMSE value of 0.0089. Furthermore, we tested the generalization ability of our proposed model on a new dataset, different from the one used for training. Even with this new dataset, our model displayed robust performance. In contrast, the other existing models achieved higher RMSE values, with the highest being 14.5092 and the lowest 0.02769. By adopting our approach, investors can trade more confidently in complex and challenging financial assets such as Bitcoin, Ethereum, and Dogecoin. Our proposed model demonstrates superior performance and generalization capabilities, providing valuable insights for participants in the cryptocurrency market. © 2023 IEEE.

关键词： Cryptocurrency deep learning ensemble learning time-series forecasting volatility

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Advancing Non-intrusive Suppression on Enhancement Distortion for Noise Robust ASR

Advancing Non-intrusive Suppression on Enhancement Distortio...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wei Wang Siyi Zhao Yanmin Qian AI Institute Department of Computer Science and Engineering Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent advancements in speech enhancement (SE) techniques have greatly improved speech clarity and intelligibility in challenging acoustic environments. However, integrating SE into automatic speech recognition (ASR) systems often results in performance degradation due to artifacts introduced during the enhancement process. While various methods have enhanced recognition accuracy in SE-ASR systems, they often require fine-tuning or re-training of SE or ASR models, which is impractical in many real-world applications. In this paper, we propose a lightweight distortion suppression (DS) network that addresses these artifacts without modifying the SE or ASR models, treating them as fixed black boxes. The DS module operates on the time-frequency (T-F) bands of the original and enhanced complex spectrograms, efficiently compensating for SE distortions using the original T-F information. We validate our approach through experiments on both Mandarin and English ASR tasks using monaural and multi-channel SE frontends, across various ASR backends. Results show that the DS module significantly improves the performance of SE-ASR systems, even when used with robust commercial ASR backends.

关键词： Degradation Time-frequency analysis Acoustic distortion Closed box Speech enhancement Acoustics Noise robustness Noise measurement Spectrogram Automatic speech recognition

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Excavation pits—progress estimation and cause of delay identification

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Construction robotics 2023年第1期7卷 53-63页

作者： Vierling, Axel Groll, Tobias Meckel, Dennis Heim, Kristina Walter, Daniel Körkemeyer, Dr. Karsten Berns, Dr. Karsten Department of Computer Science Robotics Research Lab University of Kaiserslautern-Landau Kaiserslautern Germany Department of Civil Engineering Chair in Construction Management University of Kaiserslautern-Landau Kaiserslautern Germany

This work presents a method for automated excavation speed and progress estimation. First, a measure for the progress speed of an excavation pit is taken from the literature and evaluated regarding the possibility of automation. For each possible parameter, an automated extraction algorithm is presented. The used system is an autonomous excavator arm of a backhoe loader where the used hardware and software system is described. An experimental evaluation of the presented approach has been done with the autonomous system for a small trench, including multiple digging cycles. The resulting measurements seem to include some systematic errors which could be identified and suitable sanity checks could be implemented, removing the erroneous measurements. The remaining measurements were used to determine the excavation speed of the autonomous excavator arm and compared to the values of experienced and amateur operators. The resulting speed and the relevant parameters are then sent to a web application called “DiBa” to notice a potential delay. If a measurement is smaller or larger than expected a fault identification is done in the DiBa.

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Automated quality assessment using appearance-based simulations and hippocampus segmentation on Low-field paediatric brain MR images

arXiv

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arXiv 2024年

作者： Sundaresan, Vaanathi Dinsdale, Nicola K. Department of Computational and Data Sciences Indian Institute of Science Bangalore560012 India Oxford Machine Learning in NeuroImaging Lab Department of Computer Science University of Oxford Oxford United Kingdom

Understanding the structural growth of paediatric brains is a key step in the identification of various neuro-developmental disorders. However, our knowledge is limited by many factors, including the lack of automated image analysis tools, especially in Low and Middle Income Countries from the lack of high field MR images available. Low-field systems are being increasingly explored in these countries, and, therefore, there is a need to develop automated image analysis tools for these images. In this work, as a preliminary step, we consider two tasks: 1) automated quality assurance and 2) hippocampal segmentation, where we compare multiple approaches. For the automated quality assurance task a DenseNet combined with appearance-based transformations for synthesising artefacts produced the best performance, with a weighted accuracy of 82.3%. For the segmentation task, registration of an average atlas performed the best, with a final Dice score of 0.61. Our results show that although the images can provide understanding of large scale pathologies and gross scale anatomical development, there still remain barriers for their use for more granular analyses. © 2024, CC BY.

关键词： Magnetic resonance imaging

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Policy-Guided Lazy Search with Feedback for Task and Motion Planning

Policy-Guided Lazy Search with Feedback for Task and Motion ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Mohamed Khodeir Atharv Sonwane Ruthrash Hari Florian Shkurti Robot Vision and Learning Lab University of Toronto Robotics Institute Dept. of Computer Science University of BITS Pilani Dept. of Mathematical and Computational Sciences University of Toronto

PDDLStream solvers have recently emerged as viable solutions for Task and Motion Planning (TAMP) problems, extending PDDL to problems with continuous action spaces. Prior work has shown how PDDLStream problems can be reduced to a sequence of PDDL planning problems, which can then be solved using off-the-shelf planners. However, this approach can suffer from long runtimes. In this paper we propose LAZY, a solver for PDDLStream problems that maintains a single integrated search over action skeletons, which gets progressively more geometrically informed, as samples of possible motions are lazily drawn during motion planning. We explore how learned models of goal-directed policies and current motion sampling data can be incorporated in LAZY to adaptively guide the task planner. We show that this leads to significant speed-ups in the search for a feasible solution evaluated over unseen test environments of varying numbers of objects, goals, and initial conditions. We evaluate our TAMP approach by comparing to existing solvers for PDDLStream problems on a range of simulated 7DoF rearrangement/manipulation problems. Code can be found at https://***/learning-based-tamp.

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Knowledge Distillation from Discriminative Model to Generative Model with Parallel Architecture for Speech Enhancement

Knowledge Distillation from Discriminative Model to Generati...

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International Symposium on Chinese Spoken Language Processing

作者： Tingxiao Zhou Leying Zhang Yanmin Qian Department of Computer Science and Engineering Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence Al Institute Shanghai Jiao Tong University Shanghai

ISBN: (数字)9798331516826

ISBN: (纸本)9798331516833

Generative speech enhancement methods, especially diffusion methods, have recently gained attention. However, current generative methods face several unresolved issues, such as lagging performance compared to discriminative methods on conventional enhancement metrics and slower inference speeds. In order to address these challenges, we propose a knowledge distillation approach from discriminative to generative models, and we design a parallel architecture with a latent vector fusion module to leverage their respective strengths. We conduct experiments and ablation studies on the LibriMix dataset to validate its effectiveness. The results demonstrate that our approach substantially improves performance over baseline systems in terms of speech quality and inference speed.

关键词： Measurement Speech enhancement Vectors Parallel architectures Faces

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Insights from Hyperparameter Scaling of Online Speech Separation

Insights from Hyperparameter Scaling of Online Speech Separa...

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International Symposium on Chinese Spoken Language Processing

作者： Xin Zhou Wangyou Zhang Chenda Li Yanmin Qian Department of Computer Science and Engineering Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai

ISBN: (数字)9798331516826

ISBN: (纸本)9798331516833

With the rapid development of deep learning, a large number of models with excellent performance for speech separation tasks have emerged in the literature. Despite their impressive performance, these models usually cannot be used for online speech separation tasks due to high computational and memory costs. In recent years, some approaches tailored for online speech separation have been developed to achieve state-of-the-art performance while keeping the model lightweight. However, it is often unclear how these models can scale with their architecture-specific hyperparameters as they are under-explored in the literature. To fill this gap, this paper investigates the effect of hyper-parameter scaling on the model performance, number of parameters, and computational cost based on two representative online speech separation models, BSRNN and SkiM. Experimental results on the Libri2Mix dataset reveal the relationship between model performance and composite hyperparameter configurations. We also derive the patterns of the number of parameters and computational costs varying with hyperparameters. Based on the results of the analysis, we give suggestions for scaling the two models and summarize two common trends.

关键词： Degradation Deep learning Analytical models Costs computational modeling Market research computational efficiency Speech processing Guidelines

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Band-Wise Front-End Distortion Suppression for Robust Speech Recognition

Band-Wise Front-End Distortion Suppression for Robust Speech...

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International Symposium on Chinese Spoken Language Processing

作者： Siyi Zhao Wei Wang Yanmin Qian Department of Computer Science and Engineering Auditory Cognition and Computational Acoustic Lab MoE Key Lab of Aritificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai

ISBN: (数字)9798331516826

ISBN: (纸本)9798331516833

Advancements in deep learning techniques have significantly improved automatic speech recognition (ASR). However, improving robustness to acoustic interferences, such as background noise and reverberation, remains crucial for accurate speech recognition in noisy environments. Traditional speech enhancement (SE) modules used as pre-processing front-ends often fail to integrate well with ASR models due to intelligibility distortions introduced by the SE module. Inspired by the observation adding technique and band-wise network design of recent SE models, we propose a novel adaptive band-wise observation adding (BOA) module for front-end distortion suppression. The lightweight BOA network learns time- and frequencyvariant OA factors, adaptively balancing the contributions of noisy and enhanced speech signals for diverse noisy conditions. Experiments conducted on both simulated and real-world test sets demonstrate that the BOA module produces more effective dynamic OA factors under various conditions compared to fixed OA factors, leading to improved ASR performance.

关键词： Deep learning Adaptation models Time-frequency analysis Acoustic distortion Speech enhancement Robustness Frequency diversity Noise robustness Noise measurement Reverberation

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Learning in Deep Factor Graphs with Gaussian Belief Propagation

arXiv

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arXiv 2023年

作者： Nabarro, Seth Wilk, Mark van der Davison, Andrew J. Dyson Robotics Lab Imperial College London United Kingdom Department of Computer Science University of Oxford United Kingdom

We propose an approach to do learning in Gaussian factor graphs. We treat all relevant quantities (inputs, outputs, parameters, activations) as random variables in a graphical model, and view training and prediction as inference problems with different observed nodes. Our experiments show that these problems can be efficiently solved with belief propagation (BP), whose updates are inherently local, presenting exciting opportunities for distributed and asynchronous training. Our approach can be scaled to deep networks and provides a natural means to do continual learning: use the BP-estimated posterior of the current task as a prior for the next. On a video denoising task we demonstrate the benefit of learnable parameters over a classical factor graph approach and we show encouraging performance of deep factor graphs for continual image classification. Copyright © 2023, The Authors. All rights reserved.

关键词： Contrastive Learning

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ConMamba: A Convolution-Augmented Mamba Encoder Model for Efficient End-to-End ASR Systems

ConMamba: A Convolution-Augmented Mamba Encoder Model for Ef...

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International Symposium on Chinese Spoken Language Processing

作者： Haoxiang Hou Xun Gong Yanmin Qian Department of Computer Science and Engineering Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai

ISBN: (数字)9798331516826

ISBN: (纸本)9798331516833

End-to-End Automatic Speech Recognition (ASR) models, such as Conformer, excel in accuracy but face limitations in computational complexity and positional awareness, hindering their use in real-time or resource-constrained settings. State Space Models (SSMs), particularly the Mamba model with its time-varying mechanism, offer a more efficient alternative. We propose ConMamba, a Convolution-Augmented Mamba En-coder model, which replaces the Conformer's Multi-Head Self-Attention with Mamba layers and adds convolutional layers for capturing both local and global features. Experiments on the LibriSpeech dataset show ConMamba matches traditional Conformer performance on short speech segments and outperforms them on longer ones, enhancing robustness and efficiency for practical ASR applications.

关键词： Accuracy computational modeling Face recognition Speech enhancement Transformers Robustness Real-time systems computational complexity Automatic speech recognition

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