检索结果-内蒙古大学图书馆

Intelligent Guitar Chord Recognition Using Spectrogram-Based Feature Extraction and AlexNet Architecture for Categorization

引用

International Journal of Advanced Computer science and Applications 2025年第4期16卷 758-767页

作者： Korade, Nilesh B. Salunke, Mahendra B. Bhosle, Amol A. Sangve, Sunil M. Joshi, Dhanashri M. Asalkar, Gayatri G. Kadu, Sujata R. Sarwade, Jayesh M. Department of Computer Engineering JSPM’s Rajarshi Shahu College of Engineering Tathawade Maharashtra Pune411033 India Department of Computer Engineering PCET’s Pimpri Chinchwad College of Engineering and Research Ravet Maharashtra Pune412101 India Department of Computer Science and Engineering School of Computing MIT Art Design and Technology University Loni Kalbhor Maharashtra Pune412201 India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Technology Bibwewadi Pune411037 India Department of Computer Engineering Terna Engineering College Maharashtra Mumbai India Department of Information Technology JSPM’s Rajarshi Shahu College of Engineering Tathawade Maharashtra Pune411033 India

Chord prediction plays a key role in the advancement of musical technological innovations, such as automatic music transcription, real-time music tutoring, and intelligent composition tools. Accurate chord prediction can assist musicians, educators, and developers in constructing tools that help in learning, playing, and composing music. Background noise and audio distortions may have an impact on chord prediction accuracy, particularly in real-world situations. Chords can have distinct voicings or finger positions on the guitar, resulting in slight variations in audio representation. This study focuses on the classification of guitar chords using techniques of deep learning. There are eight major and minor guitar chords in the dataset. They have been turned into spectrograms, chromagrams, and Mel Frequency Cepstral Coefficients (MFCC) so that features can be extracted. Various deep learning architectures, including CNN, ResNet50, AlexNet, and VGG, were employed to classify the chords. Experimental results demonstrated that the spectrogram-based AlexNet model outperforms others, achieving good accuracy and robustness in chord classification. The proposed study demonstrates the efficiency of spectrograms and advanced deep learning models for audio signal processing in music applications. By automating chord detection, this study provides beneficial resources for music learners as well as educators, enabling more efficient learning and real-time feedback during practice sessions. © (2025), (science and Information Organization). All Rights Reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Hybrid System with Modified Cuckoo Search to Design Airfoil

Hybrid System with Modified Cuckoo Search to Design Airfoil

引用

2023 International Conference on data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Thinakaran, K. Anand, M. Kumar, T.Rajesh Atham, Saira Banu Subramanian, K.M. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science India Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Chennai Kattankulathur India Saveetha Institute of Medical and Technical Science Saveetha School of Engineering India Presidency University School of Cse & Is Department of Computer Science and Engineering Bangaluru India Shadan College of Engineering and Technology Department of Computer Science Engineering Hyderabad India

ISBN: (纸本)9798350348910

The proposed models can design the airfoil by Cuckoo search with Levenberg-Marquardt. The Neural Network framework has impediments due to over-fitting. This paper proposed a modified cuckoo search. here the aerodynamic coefficient as an input to produce output the airfoil coordinates. The generated airfoil is compared to know its performance metrics. The Cuckoo search with the Feedforward Neural Network model yields the lowest prediction error. © 2023 IEEE.

关键词： Airfoils

来源：评论

学校读者我要写书评

暂无评论

Towards Ultra-Low-Power Neuromorphic Speech Enhancement with Spiking-FullSubNet

arXiv

引用

arXiv 2024年

作者： Hao, Xiang Ma, Chenxiang Yang, Qu Wu, Jibin Tan, Kay Chen The Department of Computing The Hong Kong Polytechnic University Hong Kong The Department of Data Science and Artificial Intelligence The Hong Kong Polytechnic University Hong Kong The Department of Data Science and Artificial Intelligence Department of Computing The Hong Kong Polytechnic University Hong Kong The Department of Electrical and Computer Engineering National University of Singapore Singapore

Speech enhancement is critical for improving speech intelligibility and quality in various audio devices. In recent years, deep learning-based methods have significantly improved speech enhancement performance, but they often come with a high computational cost, which is prohibitive for a large number of edge devices, such as headsets and hearing aids. This work proposes an ultra-low-power speech enhancement system based on the brain-inspired spiking neural network (SNN) called Spiking-FullSubNet. Spiking-FullSubNet follows a full-band and sub-band fusioned approach to effectively capture both global and local spectral information. To enhance the efficiency of computationally expensive sub-band modeling, we introduce a frequency partitioning method inspired by the sensitivity profile of the human peripheral auditory system. Furthermore, we introduce a novel spiking neuron model that can dynamically control the input information integration and forgetting, enhancing the multi-scale temporal processing capability of SNN, which is critical for speech denoising. Experiments conducted on the recent Intel Neuromorphic Deep Noise Suppression (N-DNS) Challenge dataset show that the Spiking-FullSubNet surpasses state-of-the-art methods by large margins in terms of both speech quality and energy efficiency metrics. Notably, our system won the championship of the Intel N-DNS Challenge (Algorithmic Track), opening up a myriad of opportunities for ultra-low-power speech enhancement at the edge. Our source code and model checkpoints are publicly available at ***/haoxiangsnr/spikingfullsubnet. © 2024, CC BY.

关键词： Hearing aids

来源：评论

学校读者我要写书评

暂无评论

Efficient Diabetes Detection using Hybrid Machine Learning Model

Efficient Diabetes Detection using Hybrid Machine Learning M...

引用

2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Gowri, A.S. Jose, P. Karthik, S.M. Shaheema, S Berlin Karuppasway, K.M. Balamurugan, R. School of Computing Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Department for Applied Research Gandhigram India Stella Mary's College of Engineering Department of Artificial Intelligence and Data Science Azhikal India Bharath Institute of Higher Education and Research Department of Mathematics and Statistics Chennai India Dmi College of Engineering Department of Information Technology Chennai India

ISBN: (纸本)9798350375442

Diabetes is a metabolic disease that affects a large number of the global population and is incurable. The primary causes of death symptoms are kidney failure, heart attacks, strokes, and blindness. In this paper Principal component analysis used for pre-processing the data to extract features and The Efficient Hybrid Machine Learning Model Convolution Neural Network-Support Vector Machine is used to categorize the Pima Indian dataset. CNN and non-linear SVM used in a hybrid model to classify the diabetes data from the Pima Indians. To identify positive classes of pima Indians diabetes, the HCNN-SVM extract the features from the original data expression and learns diabetes data through a novel convolution network environment to the SVM classifier with RBF function and achieve the accuracy as 98,04%. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

AI Driven Blockchain Reward Insights Using ML

AI Driven Blockchain Reward Insights Using ML

引用

Emerging Trends in Electrical, Control, and Telecommunication Engineering (ETECTE), International Conference on

作者： Abeera Malik Faisal Bukhari Hafiz Muhammad Talha Department of Information Technology Faculty of Computing and Information Technology University of the Punjab PUCIT Lahore Pakistan Department of Data Science Faculty of Computing and Information Technology University of the Punjab Lahore Pakistan

ISBN: (数字)9798331504861

ISBN: (纸本)9798331504878

Several industries have embraced blockchain technology due to the change it has brought, especially in the way records are kept. However, one major problem inherent in the concept of blockchain from the participants perspective is the presence of unpredictable rewards in proof-of-work and proof- of-stake models. That is why this research aims at developing a new strategy to solve this problem by incorporating artificial intelligence (AI) as well as machine learning (ML) algorithms into blockchain compensation prediction. This project seeks to analyze how artificial intelligence (AI) and machine learning (ML) methods may be incorporated into blockchain technology to solve the problem of the unpredictability of rewards. Using the EtherScan API, we have obtained 1000 timestamps and block reward values, and for prediction of rewards for certain timestamps, we use the KNN algorithm, linear regression algorithm, and random forest regressor algorithm. Thus, a What We KNN attained an accuracy of performance specifies, and a random forest regressor attained an impression accuracy range of 74% particular timestamps. These outcomes show the benefits of advanced ML to the blockchain ecosystem as a tool that helps stakeholders make informed decisions concerning activities like mining, staking, or investing in blockchains with potentially high rewards.

关键词： Technological innovation Machine learning algorithms Accuracy Biological system modeling Nearest neighbor methods Prediction algorithms Proof of Work Blockchains Stakeholders Random forests

来源：评论

学校读者我要写书评

暂无评论

VR-Fi: Positioning and Recognizing Hand Gestures via VR-embedded Wi-Fi Sensing

引用

IEEE Transactions on Mobile computing 2025年

作者： Wang, Hongbo Li, Xin Li, Jiachun Zhu, Haojin Luo, Jun Collaborative InitiativeInterdisciplinary Graduate Programme 639798 Singapore College of Computing and Data Science 639798 Singapore Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China

Accurate gesture-based interactions are crucial for enhancing the immersive experience in VR (virtual reality) systems;they in turn necessitate gesture positioning and recognition in physical world. However, existing VR gesture recognition methods are predominantly vision-based, incurring high computational demands and raising privacy concerns. Meanwhile, Wi-Fi-based gesture recognition methods, deemed as promising complement to vision-based ones, typically lack gesture positioning capabilities. To this end, we propose VR-Fi, a gesture positioning and recognition system leveraging VR(-headset)-embedded Wi-Fi. To position gestures across different areas, VR-Fi innovates in a frequency-hopping bandwidth expansion (FHBE) technique to improve spatial resolution for locating a target. Additionally, VR-Fi innovates in neural models to process the FHBE-enhanced Wi-Fi CSI (channel state information) and enable the multi-task requirements of the joint positioning and recognition of hand gestures. Extensive experimental results demonstrate that VR-Fi achieves a positioning accuracy of 94.47%, a recognition accuracy of 92.13%, and a joint accuracy of 89.47%. © 2002-2012 IEEE.

关键词： Frequency hopping

来源：评论

学校读者我要写书评

暂无评论

Protein Generation via Genome-scale Language Models with Bio-physical Scoring

Protein Generation via Genome-scale Language Models with Bio...

引用

2023 International Conference on High Performance computing, Network, Storage, and Analysis, SC Workshops 2023

作者： Dharuman, Gautham Ward, Logan Ma, Heng Setty, Priyanka V. Gokdemir, Ozan Foreman, Sam Emani, Murali Hippe, Kyle Brace, Alexander Keipert, Kristopher Gibbs, Thomas Foster, Ian Anandkumar, Anima Vishwanath, Venkatram Ramanathan, Arvind Data Science and Learning Division United States Argonne Leadership Computing Facility Argonne National Laboratory United States Computer Science Department University of Chicago United States Nvidia Inc. United States California Institute of Technology United States

ISBN: (纸本)9798400707858

Large language models (LLMs) trained on vast biological datasets can learn biological motifs and correlations across the evolutionary landscape of natural proteins. LLMs can then be used for de novo design of novel proteins with specific structures, functions, and physicochemical properties. We employ a pre-trained genome-scale language model that uses codons as tokens and integrate it into a workflow for targeted generation of sequences. Our framework suggests new gene sequences that are ranked for downstream evaluation by metrics that collectively capture extensive sequence-specific, biophysical, and biochemical properties. We demonstrate our integrated workflow to design novel variants of the enzyme, malate dehydrogenase (MDH), that exhibit more favorable activation energies than their natural counterparts (reduction of 4.01 kJ/mol) with sustained sequence generation rates of ~104/hr and simulation rates of ~102/hr on 64 nodes of Polaris with about 99.7 system utilization during the run. © 2023 ACM.

关键词： Physicochemical properties

来源：评论

学校读者我要写书评

暂无评论

Neural Abstractive Summarization: A Brief Survey 3

Neural Abstractive Summarization: A Brief Survey

引用

3rd IEEE International Conference on Computer Communication and Artificial Intelligence, CCAI 2023

作者： Qian, Lu Zhang, Haiyang Wang, Wei Liu, Dawei Huang, Xin Xian Jiaotong-Liverpool University School of Advanced Technology Department of Computing Suzhou China De Montfort University Cyber Technology Institute United Kingdom Taiyuan University of Technology College of Data Science Taiyuan China

ISBN: (纸本)9798350335262

Due to the development of neural networks, abstractive summarization has received more attention than extractive one, and has gained significant progress in generating fluent and human-like summaries with novel expressions. Seq2seq has become the primary framework for abstractive summarization, employing encoder-decoder architecture based on RNNs or CNNs, and Transformers. In this paper, we focus on reviewing the neural models that are based on seq2seq framework for abstractive summarization. Moreover, we discuss some of the most effective techniques for improving seq2seq models and provide two challenging directions, i.e. generating query-based abstractive summaries and incorporating commonsense knowledge, for in-depth investigation. © 2023 IEEE.

关键词： Neural networks

来源：评论

学校读者我要写书评

暂无评论

Advancements in AI-Assisted Plant Disease Diagnosis

Advancements in AI-Assisted Plant Disease Diagnosis

引用

International Conference on computing, Communication, and Intelligent Systems (ICCCIS)

作者： Anmol Chhabra Tarun Saroch Ruchika Gupta Aditya Shukla Raghav Computer Science & Engineering Department Centre of Excellence - Data Science and Cognitive Computing Chandigarh University Mohali India

Plant disease detection is a crucial task in agriculture to ensure healthy crop production. It is vital to identify plant diseases early in order to avert economic and environmental damages. A Machine learning-based approach including Deep Learning techniques for identifying plant disease was developed using Grape leaf images in this work. Inception v3, Squeezenet, VGG-16, and VGG-19 are some embedders that are used in the suggested method to convert raw image data into a fixed-length vector representation, also known as a “embedding” or “feature vector.” Our research concludes that CNN stands out with exceptional accuracy above 99.70% compared to classifiers like Naive Bayes, Logistic Regression, and SVM. Additionally, Inception V3 proved to be an efficient feature extractor, enhancing feature representation and reducing processing time.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Maxflow-Based Bounds for Low-Rate Information Propagation over Noisy Networks

arXiv

引用

arXiv 2023年

作者： Ling, Yan Hao Scarlett, Jonathan Department of Computer Science Department of Mathematics School of Computing National University of Singapore Singapore Institute of Data Science United States

We study error exponents for the problem of low-rate communication over a directed graph, where each edge in the graph represents a noisy communication channel, and there is a single source and destination. We derive maxflow-based achievability and converse bounds on the error exponent that match when there are two messages and all channels satisfy a symmetry condition called pairwise reversibility. More generally, we show that the upper and lower bounds match to within a factor of 4. We also show that with three messages there are cases where the maxflow-based error exponent is strictly suboptimal, thus showing that our tightness result cannot be extended beyond two messages without further assumptions. Copyright © 2023, The Authors. All rights reserved.

关键词： Directed graphs

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：