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Misspecified Barankin Type Bound

Misspecified Barankin Type Bound

引用

Annual conference on Information Sciences and Systems (CISS)

作者： Nadav E. Rosenthal Joseph Tabrikian School of Electrical and Computer Engineering Ben-Gurion University of the Negev Beer-Sheva Israel

ISBN: (数字)9798331513269

ISBN: (纸本)9798331513276

In statistical signal processing and estimation theory, discrepancies between the true data-generating model and the assumed model can lead to large estimation errors. The misspecified Cramér-Rao bound (MCRB) quantifies the impact of model mismatch on the mean-squared error (MSE), but it is derived for the asymptotic region, where the estimation errors are small. Consequently, it cannot be used to investigate estimation performance in the non-asymptotic region, where estimation errors are large. For instance, the MCRB is not effective in predicting the threshold phenomenon, which is crucial in many signal processing applications. The Barankin bound offers a tighter bound in the non-asymptotic region, and several works have demonstrated its applicability in predicting the threshold phenomenon. However, it is derived for perfectly specified models, where the true data-generating model matches the assumed model. In this work, a misspecified Barankin-type bound that accounts for model mismatch, enabling investigation of the impact of misspecification on the threshold phenomenon, is derived. The Barankin bound and the MCRB emerge as special cases of this lower bound. To illustrate its utility and investigate threshold signal-to-noise ratio phenomenon, we apply the proposed bound to the problem of direction-of-arrival estimation using a sensor array under model misspecification. The modeling errors induce large errors and distort the ambiguity function, a behavior effectively captured by the proposed bound.

关键词： Estimation error Lower bound Direction-of-arrival estimation Estimation theory signal processing signal to noise ratio Sensor arrays

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VisQ2SQL: Towards SQL-Driven data Visualization via LLMs-Grounded Preference Learning

VisQ2SQL: Towards SQL-Driven Data Visualization via LLMs-Gro...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Shengze Shi Tao Ren Jun Hu State Key Laboratory of Intelligent Game Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Text-to-Visualization (Text2Vis) aims to democratize data insights for non-expert users by transforming natural language query (NLQ) into visualization specification (VS). In view of the high dependence of rule-based methods on predefined VS templates and poor NLQ understanding ability of small data-driven methods, recent works resort to leveraging pre-trained LLMs to perform NLQ-understanding and VS-generating in Text2Vis tasks via prompt-guided in-context learning. However, existing LLM-based methods still fall short of satisfactory end-to-end Text2Vis performances primarily owing to the limited ability of pre-trained LLMs in directly retrieving and operating NLQ-intended tabular data. Inspired by the SQL generating ability born with latest LLMs, this paper proposes the idea of harnessing LLMs for SQL-driven visualization data retrieval and operation. Nonetheless, there remains a nonneglectable gap between data visualization queries in Text2Vis tasks and SQL retrieving queries in LLM corpus. To fill the gap, this paper proposes a Visualization Query to SQL (VisQ2SQL) framework to obtain NLQ-intended data, primarily by fine-tuning LLMs through preference learning data-retrieval SQLs induced from VS and those generated by LLMs. We conduct extensive experiments to demonstrate the superiority of VisQ2SQL over SOTA methods, and various ablation studies to verify the efficacy of VisQ2SQL.

关键词： Structured Query Language Natural languages data visualization Transforms Contrastive learning Speech enhancement signal processing data retrieval Acoustics

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CASC-XVC: Zero-Shot Cross-Lingual Voice Conversion with Content Accordant and Speaker Contrastive Losses

CASC-XVC: Zero-Shot Cross-Lingual Voice Conversion with Cont...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Han-Jie Guo Hui-Peng Du Zheng-Yan Sheng Li-Ping Chen Yang Ai Zhen-Hua Ling National Engineering Research Center of Speech and Language Information Processing University of Science and Technology of China Hefei P.R.China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Cross-lingual voice conversion (XVC) is a technology that modifies speaker identity while preserving linguistic content in scenarios where the source and target speakers use different languages. Previous non-parallel disentanglement-based methods face severe training-testing inconsistency issues in XVC tasks due to language mismatch and the lack of multilingual parallel data, which inevitably compromise the quality of the synthesized speech. In this paper, we propose CASC-XVC, a zero-shot XVC method incorporating with content accordant (CA) and speaker contrastive (SC) losses. Specifically, this method adopts the framework of FreeVC-s as the backbone. We design a cross-lingual fine-tuning process employing pairs of utterances from speakers in different languages to update the modules used in the inference stage. A CA loss and an SC loss are introduced to deal with the lack of true parallel targets in the fine-tuning process. Moreover, we use shared self-supervised learning (SSL) representations across different languages along with information perturbation for content disentanglement. Both subjective and objective results on a bilingual (English and Chinese) dataset demonstrate that our approach achieves significant improvements in XVC tasks.

关键词： Perturbation methods Self-supervised learning signal processing Acoustics Multilingual Speech processing Faces

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Fair MP-BOOST: Fair and Interpretable Minipatch Boosting

Fair MP-BOOST: Fair and Interpretable Minipatch Boosting

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Camille Olivia Little Genevera I. Allen Department of Electrical and Computer Engineering Rice University Houston Texas Department of Statistics Columbia University New York NY

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Ensemble methods, particularly boosting, have established themselves as highly effective and widely embraced machine learning techniques for tabular data. In this paper, we aim to leverage the robust predictive power of traditional boosting methods while enhancing fairness and interpretability. To achieve this, we develop FAIR MP-BOOST, a stochastic boosting scheme that balances fairness and accuracy by adaptively learning features and observations during training. Specifically, FAIR MP-BOOST sequentially samples small subsets of observations and features, termed minipatches (MP), according to adaptively learned feature and observation sampling probabilities. We devise these probabilities by combining loss functions, or by combining feature importance scores to address accuracy and fairness simultaneously. Hence, FAIR MP-BOOST prioritizes important and fair features along with challenging instances, to select the most relevant minipatches for learning. The learned probability distributions also yield intrinsic interpretations of feature importance and important observations in FAIR MP-BOOST. Through empirical evaluation of simulated and benchmark datasets, we showcase the interpretability, accuracy, and fairness of FAIR MP-BOOST.

关键词： Training Accuracy signal processing algorithms signal processing Benchmark testing Boosting Probability distribution Acoustics Ensemble learning Speech processing

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Enabling Auditory Large Language Models for Automatic Speech Quality Evaluation

Enabling Auditory Large Language Models for Automatic Speech...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Siyin Wang Wenyi Yu Yudong Yang Changli Tang Yixuan Li Jimin Zhuang Xianzhao Chen Xiaohai Tian Jun Zhang Guangzhi Sun Lu Lu Chao Zhang Tsinghua University ByteDance University of Cambridge

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Speech quality assessment typically requires evaluating audio from multiple aspects, such as mean opinion score (MOS) and speaker similarity (SIM) etc., which can be challenging to cover using one small model designed for a single task. In this paper, we propose leveraging recently introduced auditory large language models (LLMs) for automatic speech quality assessment. By employing task-specific prompts, auditory LLMs are finetuned to predict MOS, SIM and A/B testing results, which are commonly used for evaluating text-to-speech systems. Additionally, the finetuned auditory LLM is able to generate natural language descriptions assessing aspects like noisiness, distortion, discontinuity, and overall quality, providing more interpretable outputs. Extensive experiments have been performed on the NISQA, BVCC, SOMOS and VoxSim speech quality datasets, using open-source auditory LLMs such as SALMONN, Qwen-Audio, and Qwen2-Audio. For the natural language descriptions task, a commercial model Google Gemini 1.5 Pro is also evaluated. The results demonstrate that auditory LLMs achieve competitive performance compared to state-of-the-art task-specific small models in predicting MOS and SIM, while also delivering promising results in A/B testing and natural language descriptions. Our data processing scripts and finetuned model checkpoints can be found at https://***/bytedance/SALMONN.

关键词： Large language models Natural languages Speech enhancement Predictive models data processing data models Quality assessment Text to speech Internet Testing

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SPSDet: Advances in Infrared small-Scale Ship Detection Using State Space Models 24

SPSDet: Advances in Infrared Small-Scale Ship Detection Usin...

引用

Proceedings of the 2024 2nd International conference on Information Education and Artificial Intelligence

作者： Guohui Cai Huan Zhang Leihao Zhao College of Computer Science and Artificial Intelligence Southwest Minzu University Chengdu Sichuan China School of Marxism Inner Mongolia Normal University Hohhot Inner Mongolia China

ISBN: (纸本)9798400711732

Besides small vessel target detection in maritime images, other computer-vision-related challenges also remain-primarily associated with limited data concerning small targets as they are often obscured by larger objects or background noise. Therefore, Forest-Transformer-based models tend to struggle with the size of specific datasets impacting on an effective response to targets of various orientation and scales. Hence, some models need to work with more agility and less cumbersome to address these issues. In this respect, this paper proposes exciting two methods improving ocean craft detection and segmentation significantly. The first is that the lightweight YOLO v9 architecture is integrated into the SliceBoost framework using programmable gradient information to minimize loss of critical information during feature extraction. The second is the Vision Mamba model that further augments spatial awareness by embedding position information into a newly constructed bidirectional state-space model that enhances contextual modeling abilities. To adept at the modeling of small vessel detection in infrared maritime images, it combines the local feature processing efficiency of convolution-only architectures with the global knowledge aberration capabilities of Transformer-based architectures. Experimental results show significant improvements in both detection and processing efficiency making real-time small-target detection possible within varying maritime applications. Furthermore, the paper discusses possible future maritime target recognition applications of these two techniques. The source code is available at the following link: https://***/CaiGuoHui123/SPSDet.

关键词： Mamba

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Deep Support Vein Machine for Lung Parcellation

Deep Support Vein Machine for Lung Parcellation

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Haichao Peng Hao Fang Wenkang Fan Yong Wang Sunkui Ke Jie Luo Xiongbiao Luo Department of Computer Science and Technology Xiamen University Xiamen China Zhongshan Hospital Xiamen University Xiamen China Massachusetts General Hospital Harvard Medical School Boston USA National Institute for Data Science in Health and Medicine Xiamen University Xiamen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Pulmonary segments parcellation is essential to thoracoscopic segmentectomy. Surgeons manually outline pulmonary segments from preoperative images before surgery, which is a time-consuming, labor-intensive and mental-stress procedure. This work proposes a novel small learning model of deep support vein machine without using annotated pulmonary segments data for automatic lung parcellation. Specifically, this machine can learn anatomical structures of pulmonary lobe, bronchus, artery, and vein by two cascade multilayer perceptrons to automatically divides the lung into eighteen segments. The perceptron module typically smooths the boundary of the pulmonary segment to attain robust and precise parcellation. Additionally, three new metrics are defined to quantitatively evaluate the quality of lung parcellation. We validate our methods on 108 clinical pulmonary computed tomography scans, with the experimental results showing that our proposed machine certainly outperforms current methods and provides a promising way to fully automated lung parcellation. Particularly, the dice similarity coefficient of lung parcellation was significantly improved from 0.886 to 0.918.

关键词： Measurement Image segmentation Lungs Computed tomography Surgery signal processing Multilayer perceptrons Brain modeling data models Speech processing

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Demonstration: Real-Time mmWave Integrated Communication and Sensing

Demonstration: Real-Time mmWave Integrated Communication and...

引用

IEEE conference on Wireless Communications and Networking

作者： Markus Petri Nebojsa Maletic IHP - Leibniz-Institut für innovative Mikroelektronik Frankfurt (Oder) Germany

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The proposed demonstration shows a real-time integrated communication and sensing (ICAS) system, operating in the millimeter-wave (mmWave) frequency band at a center frequency of 60 GHz. The demonstrated system allows bidirectional data communication with a coded data rate of up to 2.6 Gbit/s as well as mono-static radio detection and ranging (RADAR) with a range resolution of 6.7 cm and up to 2.1 kHz sensing rate. According to the basic principles of ICAS, the same hardware resources, the same signal waveform and the same frequency channel are used for both communication and sensing. All necessary functionality and the whole digital signal processing is integrated in one system-on-chip, resulting in a small form factor of the transceiver stations.

关键词： Radar detection Millimeter wave radar Real-time systems Transceivers Hardware Distance measurement Sensors System-on-chip Millimeter wave communication signal resolution

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Detecting Deceptive Narratives in Social Media Using Advanced Natural Language and signal processing Techniques

Detecting Deceptive Narratives in Social Media Using Advance...

引用

Intelligent Control, Computing and Communications (IC3), International conference on

作者： Manjunatha Ginni Nijhawan B. Veerasekhar Reddy Rusul Najm Maneesh Upadhya Karibandi Venkata Sai Lakshmi Harika Department of Mechanical Engineering New Horizon College of Engineering Bangalore India Lovely Professional University Phagwara Department of Information Technology MLR Institute of Technology Hyderabad Telangana India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq Department of Electrical Electronics & Communication Engineering Galgotias university Gerater Noida Uttar Pradesh Department of CSE GRIET Bachupally Hyderabad Telangana India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

The recent spread of fake news through social media has raised very crucial issues of information credibility. The current study presents an employ of signal processing methodology., natural language processing (NLP) for the scheduling of the fake news detection system. The Kaggle Fake News Challenge dataset is used in this research; it provides a rich set of news articles labeled as fake and real. The proposed technique has been called NLP-SignaIFusion., and it applies TF-IDF and wordPEDES for the extraction of textual features from the opinion and sentiment data. Sentiment analysis is used as feature extraction method in this context in order to detect the affective content which fulfils the aim to suggest markers of deception. Logistic regression and neural networks classifiers are applied to categorize the news articles. The targets I employed to perform the model assessment include accuracy., the receiver operating characteristic- area under curve., and the confusion matrix. The integration of signal processing with NLP optimizes the fake news detection and provides new avenues for understanding the differences between anything fake news in social medial.

关键词： Sentiment analysis Logistic regression Accuracy Social networking (online) Neural networks Machine learning Feature extraction Fake news signal detection Sorting

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Aircraft Fuel Flow Modeling and Performance Optimization Using Machine Learning 3rd

Aircraft Fuel Flow Modeling and Performance Optimization Usi...

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3rd International conference on signal and data processing, ICSDP 2023

作者： Dharrao, Deepak Misal, Mrinank Rai, Naivedya Joshi, Nirgoon Rajagopalan, Siddharth Pangavhane, Madhuri Bongale, Anupkumar Department of Computer Science and Engineering Symbiosis Institute of Technology Pune Campus Symbiosis International (Deemed University) Pune India Department of Information Technology Symbiosis Institute of Technology Pune Campus Symbiosis International (Deemed University) Pune India Symbiosis Institute of Technology Pune Campus Symbiosis International (Deemed University) Pune India Department of Artificial Intelligence and Machine Learning Symbiosis Institute of Technology Pune Campus Symbiosis International (Deemed University) Pune India

ISBN: (纸本)9789819795772

The modeling of fuel flow in aircraft is a critical element in evaluating their performance. However, many of the existing approaches rely on highly aggregated data, which may not provide the necessary accuracy required by the aviation industry. To address this challenge, a new data-driven approach is proposed in this study. This method leverages machine-learning techniques and uses full-flight data from aircraft sensors to develop fuel flow models. The approach focuses on identifying the features that impact fuel flow rates during different flight phases, using a unique deep-learning approach for modeling. The results demonstrate a significant improvement over traditional practices, providing a more comprehensive and accurate alternative for performance evaluations. As a result, airlines and maintenance providers can optimize their operations and reduce fuel costs while meeting environmental targets. The proposed method achieved a 99.25% adjusted R2 score, highlighting the potential of machine learning tools in developing accurate and detailed fuel flow models based on full-flight data, providing significant benefits for the aviation industry. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fuel consumption and emissions Fuel flow models Full-flight data Multi-layer perceptron (MLP) Optimization of airline operations

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