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LyricJam Sonic: A Generative System for Real-Time Composition and Musical Improvisation 12th

LyricJam Sonic: A Generative System for Real-Time Compositio...

12th International Conference on Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART) Held as Part of EvoStar Conference

作者： Vechtomova, Olga Sahu, Gaurav Univ Waterloo Waterloo ON Canada

ISBN: (纸本)9783031299551;9783031299568

Electronic music artists and sound designers have unique workflow practices that necessitate specialized approaches for developing music information retrieval and creativity support tools. Furthermore, electronic music instruments, such as modular synthesizers, have near-infinite possibilities for sound creation and can be combined to create unique and complex audio paths. The process of discovering interesting sounds is often serendipitous and impossible to replicate. For this reason, many musicians in electronic genres record audio output at all times while they work in the studio. Subsequently, it is difficult for artists to rediscover audio segments that might be suitable for use in their compositions from thousands of hours of recordings. In this paper, we describe LyricJam Sonic, a creative tool for musicians to rediscover their previous recordings, re-contextualize them with other recordings, and create original live music compositions in real-time. A bi-modal AI-driven approach uses generated lyric lines to find compatible audio clips from the artist's past studio recordings, and uses them to generate new lyric lines, which in turn are used to find other clips, thus creating a continuous and evolving stream of music and lyrics. The intent is to keep the artists in a state of creative flow conducive to music creation rather than taking them into an analytical/critical state of deliberately searching for past audio segments. The system can run in either a fully autonomous mode without user input, or in a live performance mode, where the artist plays live music, while the system "listens" and creates a continuous stream of music and lyrics in response (LyricJam Sonic: https://*** Demo videos: https://***/view/supplementarymaterial-for-evo/home).

关键词： Generative Music Lyrics Generation Neural Network variational autoencoder Generative Adversarial Network

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DefWeb: Defending User Privacy against Cache-based Website Fingerprinting Attacks with Intelligent Noise Injection 23

DefWeb: Defending User Privacy against Cache-based Website F...

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39th Annual Computer Security Applications Conference (ACSAC)

作者： Son, Seonghun Dipta, Debopriya Roy Gulmezoglu, Berk Iowa State Univ Ames IA 50011 USA

ISBN: (纸本)9798400708862

Cache-based website fingerprinting (WF) attacks violate user privacy where the attacker leverages the shared last-level cache in CPUs and analyzes the fingerprints through machine learning and deep learning models. WF attacks are even applicable in Incognito and anonymized browser platforms, leading to a serious threat to the public. Several defense techniques inject random noise during website rendering to degrade the attack success rate, while these techniques either create large performance overhead or cannot obfuscate the WF dataset entirely when the attacker retrains a new learning model with noisy fingerprints. In this work, we develop a dynamic generative learning-based defense technique, DefWeb, to protect user privacy against cache-based WF attacks by injecting precise noise into the WFs. For this purpose, (i) we train generative neural networks to represent high-dimensional fingerprints in a low-dimension space while creating distinct clusters for each website. (ii) Minimal noise templates are extracted in the low-dimension space to obfuscate the fingerprints efficiently. (iii) We create practical noise templates that can be added to WFs during website rendering by leveraging self-modifying code (SMC). We implement DefWeb in both simulation and real-world setups to degrade the attacker's model accuracy. DefWeb can decrease the model accuracy to 1.1% and 28.8% in simulation and real-world setups, respectively, including Mozilla Firefox, Google Chrome, and Tor browsers. Finally, the performance overhead introduced by DefWeb is only 9.5%, which is considerably lower than previous defense techniques.

关键词： Cache-based Attack Noise Injection Website Fingerprinting Generative Learning variational autoencoder Web Security

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Joint Inversion of Electrical Impedance, Microwave and Ultrasonic Data With Structural Feature Fusion for Human Thorax Imaging

Joint Inversion of Electrical Impedance, Microwave and Ultra...

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IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization (NEMO)

作者： Lin, Zhichao Guo, Rui Zhang, Ke Zhang, Haolin Li, Maokun Yang, Fan Xu, Shenheng Abubakar, Aria Beijing Natl Res Ctr Informat Sci & Technol BNRis Beijing Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing Peoples R China Schlumberger Houston TX USA

ISBN: (纸本)9798350347401

In this study, we propose a novel joint inversion method for human thorax imaging that combines electrical impedance tomography (EIT), microwave tomography (MT), and ultrasound tomography (UT) through structural feature fusion. The structural and value features of the three modality images are decoupled and encoded into structure code and value code by a disentangled variational autoencoder (DVAE). The inversion is then performed at the feature level, with the structure code and value code treated as unknown parameters to be inverted under a deterministic framework by the Gauss-Newton method. To ensure the consistency of the structures in the three modalities, the structure codes of the three modalities are averaged every few iterations during the inversion process. Numerical results demonstrate an improved performance of our joint inversion approach over traditional separate inversion methods in human thorax imaging.

关键词： Electrical impedance tomography microwave joint inversion thorax imaging ultrasound variational autoencoder

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Answer selection in community question answering exploiting knowledge graph and context information

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SEMANTIC WEB 2022年第3期13卷 339-356页

作者： Boroujeni, Golshan Afzali Faili, Heshaam Yaghoobzadeh, Yadollah Univ Tehran Coll Engn Sch Elect & Comp Engn Tehran Iran Inst Res Fundamental Sci IPM Sch Comp Sci Tehran Iran

With the increasing popularity of knowledge graph (KG), many applications such as sentiment analysis, trend prediction, and question answering use KG for better performance. Despite the obvious usefulness of commonsense and factual information in the KGs, to the best of our knowledge, KGs have been rarely integrated into the task of answer selection in community question answering (CQA). In this paper, we propose a novel answer selection method in CQA by using the knowledge embedded in KGs. We also learn a latent-variable model for learning the representations of the question and answer, jointly optimizing generative and discriminative objectives. It also uses the question category for producing context-aware representations for questions and answers. Moreover, the model uses variational autoencoders (VAE) in a multi-task learning process with a classifier to produce class-specific representations for answers. The experimental results on three widely used datasets demonstrate that our proposed method is effective and outperforms the existing baselines significantly.

关键词： Community question answering knowledge graph context convolutional-deconvolutional variational autoencoder

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Efficient Local Image Descriptors Learned With autoencoders

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IEEE ACCESS 2022年 10卷 221-235页

作者： Zizakic, Nina Pizurica, Aleksandra Univ Ghent TELIN GAIM Dept Telecommun & Informat Proc B-9000 Ghent Belgium

Local image descriptors play a crucial role in many image processing tasks, such as object tracking, object recognition, panorama stitching, and image retrieval. In this paper, we focus on learning local image descriptors in an unsupervised way, using autoencoders and variational autoencoders. We perform a thorough comparative analysis of these two approaches along with an in-depth analysis of the most relevant hyperparameters to guide their optimal selection. In addition to this analysis, we give insights into the difficulties and the importance of selecting right evaluation techniques during the unsupervised learning of the local image descriptors. We explore the extent to which a simple perceptual metric during training can predict the performance on tasks such as patch matching, retrieval and verification. Finally, we propose an improvement to the encoder architecture that yields significant savings in memory complexity, especially in single-image tasks. As a proof of concept, we integrate our descriptor into an inpainting algorithm and illustrate its results when applied to the virtual restoration of master paintings. The source code required to reproduce the presented results has been made available as a repository on GitHub (https://***/nimpy/local-img-descr-ae).

关键词： Task analysis Computer architecture Benchmark testing Training Deep learning Object tracking Neural networks Local image descriptor autoencoder variational autoencoder inpainting unsupervised deep learning

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variational cold-start resistant recommendation

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INFORMATION SCIENCES 2022年 605卷 267-285页

作者： Walker, Joojo Zhang, Fengli Zhong, Ting Zhou, Fan Baagyere, Edward Yellakuor Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu Peoples R China CK Tedam Univ Technol & Appl Sci Dept Comp Sci Navrongo Ghana

Conventionally, cold-start limitations are managed by leveraging side information such as social-trust relationships. However, the relationships between users in social networks are complex, uncertain, and sparse. Therefore, it is necessary to extract beneficial social connections to make the recommendation models cold-start resistant. Towards this end, we propose a novel recommendation model called variational Cold-start Resistant Recommendation (CORE-VAE). More concretely, we employ a social-aware similarity function and a graph convolutional network (GCN) to generate robust social-aware user representations that account for the complexities, uncertainties, and sparse nature of the social-trust network. Subsequently, these powerful social-aware representations aid us in producing cold-start resistant rating vectors for all users. To explore the rich user rating information, we propose an expressive variational autoencoder (VAE) model. Unlike earlier VAE-based CF models, CORE-VAE utilizes a novel prior distribution and a well-designed skip-generative network to alleviate the posterior collapse issue considerably. Besides, CORE-VAE can also capture the latent space's uncertainty and ensure that observations and their accompanying latent variables have high mutual information. Overall, these novel techniques dramatically help produce better latent representations for generating more accurate recommendations. We show that CORE-VAE outperforms numerous competitive baseline models on real-world datasets through comprehensive empirical evaluation and analysis. (C) 2022 Elsevier Inc. All rights reserved.

关键词： variational autoencoder Cold-start Social-trust information Graph convolutional network

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Inverse estimation method for internal defects based on surface stress of carbon-fiber-reinforced plastics using machine learning

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ADVANCED COMPOSITE MATERIALS 2022年第6期31卷 617-629页

作者： Kojima, Yuta Hirayama, Kenta Endo, Katsuhiro Hiraide, Kazuya Muramatsu, Mayu Keio Univ Grad Sch Dept Sci Open & Environm Syst Yokohama Kanagawa Japan Keio Univ Dept Mech Engn Yokohama Kanagawa Japan

Carbon-fiber-reinforced plastic (CFRP) is a composite material whose base material is plastic and reinforcement material is carbon fibers. CFRP is widely used in various fields for laminating prepregs. The laminated plate tends to sustain damage, such as delamination, fiber breakage, and base material breakage;hence, we must conduct high-precision and efficient nondestructive testing (NDT). Examples of NDT are ultrasonic examination, X-ray tomography, and infrared stress analysis. With most NDT methods, it is difficult to easily obtain detailed correct information of defects, such as their depth, position, and size. To solve this problem, we develop a machine-learning-aided inverse analysis model that predicts the spatial information of defects from the sum of the principal stresses on the surface calculated from the temperature change measured by infrared analysis, and we propose it as an alternative method to the existing damage analysis. Applying the proposed method to the simulated stress distributions of quasi-isotropic CFRP laminates with defects, the results showed over 99% success to recognize the detail information of defects. Additionally, we examine the properties of the dataset using a forward analysis model and a variational autoencoder. Our method with a convolutional neural network enables us to successfully estimate the information of defects at high speed. Experimental data can be applicable as well as the simulation results to our proposed method, and we believe our method will be a powerful supporting tool for the current NDT for CFRPs.

关键词： machine learning non-destructive testing finite element method carbon-fiber-reinforced plastic convolutional neural network variational autoencoder

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One-shot emotional voice conversion based on feature separation

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SPEECH COMMUNICATION 2022年 143卷 1-9页

作者： Lu, Wenhuan Zhao, Xinyue Guo, Na Li, Yongwei Wei, Jianguo Tao, Jianhua Dang, Jianwu Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Qinghai Minzu Univ Sch Comp Xining Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Beijing Peoples R China Japan Adv Inst Sci & Technol Nomi Japan

The task of emotional voice conversion (EVC) aims to convert speech from one emotional state into another, while keeping linguistic content, speaker identity and other emotion-independent information unchanged. Because previous studies were limited to a specific set of emotions, it is challenging to realize the conversion of emotions never seen in training stage. In this paper, we propose a one-shot emotional voice conversion model based on feature separation. The proposed method could control emotional characteristics with Global Emotion Embeddings (GEEs), and introduce activation guidance (AG) and mutual information (MI) minimization to reduce the correlations between emotion embedding and emotion-independent representation. At run-time conversion, it could produce the desired emotional utterance from a single pairwise utterance without any emotion labels, whether the target emotion appears in the training set or not. The subjective and objective evaluations validate the effectiveness of our proposed model for both the degree of feature separation and emotion expression, even it could achieve unseen emotion conversion.

关键词： Emotional voice conversion Feature separation Global emotion embeddings variational autoencoder Valence-arousal model

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dB: A Web-based Drummer Bot for Finger-Tapping

dB: A Web-based Drummer Bot for Finger-Tapping

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International Conference on New Interfaces for Musical Expression, NIME 2024

作者： Erdem, Çağrı Griwodz, Carsten Department of Informatics University of Oslo Oslo Norway

dB is a web-based interface that serves as a "drummer bot" for exploring interactive groove-making experiences with an AI percussion system. This system, leveraging variational autoencoders (VAEs), transforms simple rhythmic inputs into complex drum patterns with microtiming and dynamics. Designed for accessibility and playfulness, dB is easily operated via a computer keyboard, making it suitable for a wide range of users. This paper outlines dB’s foundational concepts, data collection, and a comprehensive overview of system and interface architecture. We then present our preliminary user study that investigated specific aspects of user engagement, including joy and boredom states, as well as perceptions of effort and control. The study’s results underscore the musical background, expertise, and generational differences as significant influences on user experiences. Notably, test conditions characterized by greater randomness and rhythmic variation were consistently perceived as more engaging, and emerging trends were observed in user responses diverging over time. © 2024, International Conference on New Interfaces for Musical Expression. All rights reserved.

关键词： generative models Human-AI collaboration rhythm pattern generation user studies variational autoencoder

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Deep Unsupervised Clustering for Conditional Identification of Subgroups Within a Digital Pathology Image Set 26th

Deep Unsupervised Clustering for Conditional Identification ...

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26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)

作者： Sidulova, Mariia Sun, Xudong Gossmann, Alexej US FDA Ctr Devices & Radiol Hlth Silver Spring MD 20993 USA Helmholtz Munich Inst AI Hlth Munich Germany George Washington Univ Dept Biomed Engn Washington DC USA

ISBN: (纸本)9783031439926;9783031439933

Consideration of subgroups or domains within medical image datasets is crucial for the development and evaluation of robust and generalizable machine learning systems. To tackle the domain identification problem, we examine deep unsupervised generative clustering approaches for representation learning and clustering. The variational Deep Embedding (VaDE) model is trained to learn lower-dimensional representations of images based on a Mixture-of-Gaussians latent space prior distribution while optimizing cluster assignments. We propose the Conditionally Decoded variational Deep Embedding (CDVaDE) model which incorporates additional variables of choice, such as the class labels, as conditioning factors to guide the clustering towards subgroup structures in the data which have not been known or recognized previously. We analyze the behavior of CDVaDE on multiple datasets and compare it to other deep clustering algorithms. Our experimental results demonstrate that the considered models are capable of separating digital pathology images into meaningful subgroups. We provide a general-purpose implementation of all considered deep clustering methods as part of the open source Python package DomId (https://***/DIDSR/DomId).

关键词： Domain Identification Deep Clustering Subgroup Identification variational autoencoder Generative Model

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