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14th Annual Summit and Conference of the Asia-Pacific-Signal-and-Information-Processing-Association (APSIPA ASC)

作者： Tran, Dung Kim Akagi, Masato Unoki, Masashi Japan Adv Inst Sci & Technol Grad Sch Adv Sci & Technol 1-1 Asahidai Nomi Ishikawa 9231292 Japan

ISBN: (数字)9786165904773

ISBN: (纸本)9786165904773

Deep hashing algorithms used for speaker identification and retrieval aim to produce discriminative hash codes for a set of speech signals. The basis of this task is highly related to speaker individuality. However, existing deep speaker hashing algorithms were constructed without considering speaker individuality. Previous studies have demonstrated the importance of speaker individuality in speech analysis and synthesis applications. Furthermore, recent studies have demonstrated the advantages of sparse representations of speech signals over the traditional spectrograms. This paper proposes a method for hashing the significant acoustical features related to speaker individuality by using auditory sparse representations. In speaker identification and speaker retrieval experiments with the VoxCeleb2 dataset, our 64-bit hash codes achieved 99.91% in top-1 accuracy and 97.55% in MAP@100, which are highly competitive with other state-of-the-art methods.

关键词： Auditory filterbank perceptual features gammatone/gammachirp filter matching pursuit sparse coding

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Cosmic Ray Detection in Astronomical Images via Dictionary Learning and sparse Representation 30

Cosmic Ray Detection in Astronomical Images via Dictionary L...

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30th European Signal Processing Conference (EUSIPCO)

作者： Bhavanam, Srinadh Reddy Channappayya, Sumohana S. Srijith, P. K. Desai, Shantanu Indian Inst Technol Kandi 502285 Telangana India

ISBN: (纸本)9789082797091

In this work, we propose a novel Dictionary Learning (DL) based framework to detect Cosmic Ray (CR) hits that contaminate the astronomical images obtained through optical photometric surveys. The unique and distinguishable spatial signatures of CR hits compared to other actual astrophysical sources in the image motivated us to characterize the CR patches uniquely via their sparse representations obtained from a learned dictionary. Specifically, the dictionary is trained on images acquired from the Dark Energy Camera (DECam) observations. Next, the learned dictionary is used to represent the CR and Non-CR patches (e.g., each patch is with 11x11 pixel resolution) extracted from the original images. A Machine Learning (ML) classifier is then trained to classify the CR and Non-CR patches. Empirically, we demonstrate that the proposed DL-based method can detect the CR hits at patch level and provide approximately 83% detection rates at 0.1 % false positives on the DECam test data with Random Forest (RF) algorithm. Further, we used the coarse segmentation maps obtained from the classifier output to guide the deep-learning-based CR segmentation models. The coarse maps are fed through a separate channel along with the contaminated image to detect the CR-induced pixels more accurately. We evaluated the performance of proposed DL-guided deep segmentation models over the baseline on test data from DECam. We demonstrate that the proposed method provides additional guidance to the baseline models in terms of faster convergence rate and improves CR detection performance by 2% in the case of shallow models. We made our dataset and models available at https://***/lfovia/Dictionary-Learning-Augmented-Cosmic-Ray-Detection.

关键词： Cosmic ray hits observational astronomy image processing dictionary learning approximate K-SVD sparse coding

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MULTITASK sparse NEURAL NETWORK FOR HYPERSPECTRAL IMAGE DENOISING 47

MULTITASK SPARSE NEURAL NETWORK FOR HYPERSPECTRAL IMAGE DENO...

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47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Xiong, Fengchao Ye, Minchao Zhou, Jun Lu, Jianfeng Qian, Yuntao Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Peoples R China China Jiliang Univ Coll Informat Engn Hangzhou Peoples R China Griffith Univ Sch Informat & Commun Technol Nathan Qld Australia Zhejiang Univ Coll Comp Sci Hangzhou Peoples R China

ISBN: (纸本)9781665405409

Data-driven deep learning (DL)-based methods directly learn the nonlinear mapping between noisy hyperspectral images (HSIs) and corresponding clean ones. However, DL-based methods neglect the prior knowledge of HSIs embodied by physical models. Consequently, they require complex network architectures and a large number of training samples. To address the above issues, this paper introduces a multitask sparse neural network (MTSNN) which bridges the sparsity prior of HSIs with data-driven deep learning for HSI denoising. Specifically, we first build a multitask sparse (MTS) denoising model which shares sparse coefficients among bands to exploit the spectral-spatial correlation and learns a dictionary for each band to depict the distinct spatial structure among bands. The iterative optimization of the MTS model is then unfolded to yield our MTSNN by introducing some learnable parameters. MTSNN is a multi-branch network. Each branch performs a single denoising task for an individual band. All branches are connected by shared coefficients, forming multitask denoising for all bands. The hybrid advantages of the MTS model and data-driven learning equip MTSNN with strong denoising ability, preferable learning capability, superior interpretability, and higher generalization capacity. Experimental results demonstrate that our method achieves state-of-the-art denoising performance compared with several alternative approaches.

关键词： Hyperspectral image denoising model-based neural network multitask learning sparse coding

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Satellite Recognition via sparse coding Based Probabilistic Latent Semantic Analysis

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INTERNATIONAL JOURNAL OF HUMANOID ROBOTICS 2014年第2期11卷

作者： Zhao, Danpei Lu, Ming Zhang, Xuguang Shi, Jun Jiang, Zhiguo Beihang Univ Sch Astronaut Image Proc Ctr Beijing 100191 Peoples R China Yanshan Univ Inst Elect Engn Key Lab Ind Comp Control Engn Hebei Prov Qinghuadao 066004 Peoples R China

Traditional satellite recognition usually applies low-level features (e.g., invariant moment) to describe the global information of satellites. Therefore, the local property and the latent high-level semantic concepts of satellites are likely to be ignored. Recently, the topic model has been given more attention in object recognition field, which generally supposes that the local feature can be modeled as words and images are regarded as documents. Based on this assumption, it aims to discover the latent topics existed between words and documents and then utilizes the topics to represent the images. However, topic model often uses bag-of-words (BoW) strategy that each local feature descriptor is represented by only one word. In this paper, we propose a novel method called sparse coding (SC) based probabilistic latent semantic analysis (SC-pLSA) for satellite recognition. Compared with conventional topic models, our method performs sparse coding to explore the potential correlation between the descriptor and multiple words. Consequently, the descriptor can be described by a small set of words. Based on this word-level representation, probabilistic latent semantic analysis (pLSA) model with simplicity and low computational cost is employed for learning the latent topics. Experimental results on the BUAA-SID 1.0 multi-view satellite dataset validate the effectiveness of our proposed method, and especially under the interference of noise, it outperforms the traditional recognition methods: Hu invariant moment, BoW, SC, locality-constrained linear coding (LLC), conventional pLSA and latent Dirichlet allocation (LDA) with three different classifiers: nearest neighbor (NN) classifier, linear SVM classifier, and sparse representation classifier (SRC).

关键词： Satellite recognition topic model sparse coding probabilistic latent semantic analysis BUAA-SID 1.0

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A geometry constrained dictionary learning method for industrial process monitoring

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INFORMATION SCIENCES 2021年 546卷 265-282页

作者： Huang, Keke Wen, Haofei Liu, Han Yang, Chunhua Gui, Weihua Cent South Univ Sch Automat Changsha 410083 Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China

Data-driven process monitoring methods have attracted great attention due to the case that it can provide an efficient way to cope with the industrial process without the need of first-principle models. The local information that results from the reaction process often influences the process monitoring result. Unfortunately, this local information is usually ignored in the data-driven process monitoring method. In this paper, a geometry con-strained dictionary learning (GCDL) method is proposed to address the above problem. By exploiting the underlying characteristics and simultaneously holding the local geometrical information, the GCDL method leads to a balance between the reconstructive item and the discriminative item. Inspired by the manifold method, the GCDL method can provide discriminative sparse coding, which means that samples that belong to the same class will share similar sparse coding. It is in accordance with the fact that the samples of different categories often reside on different subspaces embedded in a high-dimensional feature space. The effectiveness of the proposed method is evaluated through two numerical simulation cases, a continuous stirred tank reactor (CSTR) case and a real industrial aluminum electrolysis process. Extensive experimental results reveal that the proposed method shows better performance by taking the local geometrical information into consideration. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Process monitoring Local and global dictionary learning Manifold learning sparse coding

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An enhanced sparse autoencoder for machinery interpretable fault diagnosis

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MEASUREMENT SCIENCE AND TECHNOLOGY 2024年第5期35卷 055108-055108页

作者： Niu, Maogui Jiang, Hongkai Wu, Zhenghong Shao, Haidong Northwestern Polytech Univ Sch Civil Aviat Xian 710072 Peoples R China Hunan Univ Coll Mech & Vehicle Engn Changsha 410082 Peoples R China

The interpretability of individual components within existing autoencoders remains insufficiently explored. This paper aims to address this gap by delving into the interpretability of the encoding and decoding structures and their correlation with the physical significance of vibrational signals. To achieve this, the sparse coding with Multi-layer Decoders (SC-MD) model is proposed, which facilitates fault diagnosis from two perspectives: the working principles of the model itself and the evolving trends of fault features. Specifically, a sparse coding protocol to prevent L1-norm collapse is proposed in the encoding process, regularizing the encoding to ensure that each latent code component possesses variance greater than a fixed threshold on a set of sparse representations given the input data. Subsequently, a multi-layer decoder structure is designed to capture the intricate mapping relationship between features and fault patterns. Finally, the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) is employed as the solver for the SC-MD model, enabling end-to-end updates of all parameters by unfolding FISTA. The coherent theoretical framework ensures the interpretability of SC-MD. Utilizing aeroengine bearing data, we demonstrate the exceptional performance of our proposed approach under both normal conditions and intense noise, as compared to state-of-the-art deep learning methods.

关键词： sparse coding multi-layer decoders fault diagnosis aircraft engine bearing data fast iterative shrinkage-thresholding algorithm

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Unsupervised denoising of the non-invasive fetal electrocardiogram with sparse domain Kalman filtering and vectorcardiographic loop alignment

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PHYSIOLOGICAL MEASUREMENT 2024年第7期45卷 075006-075006页

作者： de Vries, I. R. van Laar, J. O. E. H. van der van der Jagt, M. B. Vullings, R. Maxima Med Ctr Dept Obstet & Gynecol Veldhoven Netherlands Eindhoven Univ Technol Dept Elect Engn Eindhoven Netherlands Eindhoven Univ Technol Dept Biomed Engn Eindhoven Netherlands Nemo Healthcare BV Veldhoven Netherlands

Objective. Even though the electrocardiogram (ECG) has potential to be used as a monitoring or diagnostic tool for fetuses, the use of non-invasive fetal ECG is complicated by relatively high amounts of noise and fetal movement during the measurement. Moreover, machine learning-based solutions to this problem struggle with the lack of clean reference data, which is difficult to obtain. To solve these problems, this work aims to incorporate fetal rotation correction with ECG denoising into a single unsupervised end-to-end trainable method. Approach. This method uses the vectorcardiogram (VCG), a three-dimensional representation of the ECG, as an input and extends the previously introduced Kalman-LISTA method with a Kalman filter for the estimation of fetal rotation, applying denoising to the rotation-corrected VCG. Main results. The resulting method was shown to outperform denoising auto-encoders by more than 3 dB while achieving a rotation tracking error of less than 33 degrees. Furthermore, the method was shown to be robust to a difference in signal to noise ratio between electrocardiographic leads and different rotational velocities. Significance. This work presents a novel method for the denoising of non-invasive abdominal fetal ECG, which may be trained unsupervised and simultaneously incorporates fetal rotation correction. This method might prove clinically valuable due the denoised fetal ECG, but also due to the method's objective measure for fetal rotation, which in turn might have potential for early detection of fetal complications.

关键词： vectorcardiogram fetal movement Kalman filtering sparse coding

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3D Skeletal Gesture Recognition via Discriminative coding on Time-Warping Invariant Riemannian Trajectories

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IEEE TRANSACTIONS ON MULTIMEDIA 2021年 23卷 1841-1854页

作者： Liu, Xin Zhao, Guoying Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China Univ Oulu Ctr Machine Vis & Signal Anal FI-90014 Oulu Finland Northwest Univ Sch Informat & Technol Xian Peoples R China

Learning 3D skeleton-based representation for gesture recognition has progressively stood out because of its invariance to the viewpoint and background dynamics of video. Typically, existing techniques use absolute coordinates to determine human motion features. The recognition of gestures, however, is irrespective of the position of the performer, and the extracted features should be invariant to body size. In addition, when comparing and classifying gestures, the problem of temporal dynamics can greatly distort the distance metric. In this paper, we represent a 3D skeleton as a point in the special orthogonal group SO(3) product space that expressly models the 3D geometric relationships between body parts. As such, a gesture skeletal sequence can be described by a trajectory on a Riemannian manifold. Following that, we propose to generalize the transported square-root vector field to obtain a time-warping invariant metric for comparing these trajectories (identifying these gestures). Moreover, by specifically considering the labeling information with encoding, a sparse coding scheme of skeletal trajectories is presented to enforce the discriminant validity of atoms in the dictionary. Experimental results indicate that the proposed approach has achieved state-of-the-art performance on many challenging gesture recognition benchmarks.

关键词： 3D skeleton representation gesture recognition Riemannian geometry sparse coding time-warping invariant feature

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PARAMETER ESTIMATION IN sparse INVERSE PROBLEMS USING BERNOULLI-GAUSSIAN PRIOR 47

PARAMETER ESTIMATION IN SPARSE INVERSE PROBLEMS USING BERNOU...

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47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Barbault, Pierre Kowalski, Matthieu Soussen, Charles Univ Paris Saclay L2S CNRS Cent Supelec Gif Sur Yvette France

ISBN: (纸本)9781665405409

sparse coding is now one of the state-of-art approaches for solving inverse problems. In combination with (Fast) Iterative Shrinkage Thresholding Algorithm (ISTA), among other algorithms, one can efficiently get a nice estimator of the sought sparse signal. However, the major drawback of these methods is the tuning of the so-called hyperparameter. In this paper, we first provide an Expectation-Maximization (EM) algorithm to estimate the parameters of a Bernoulli-Gaussian model for denoising a sparse signal corrupted by a white Gaussian noise. Then, building on the ExpectationMaximization interpretation of ISTA, we provide a simple iterative algorithm to blindly estimate all the model parameters in the linear inverse problem context, including the hyperparameter involved in the popular l(0) regularized minimization. Moreover, the algorithm directly yields an estimator of the sparse signal.

关键词： sparse coding Inverse Problem ISTA Bernoulli-Gaussian model

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MINIMIZING RESIDUALS FOR NATIVE-NONNATIVE VOICE CONVERSION IN A sparse, ANCHOR-BASED REPRESENTATION OF SPEECH 47

MINIMIZING RESIDUALS FOR NATIVE-NONNATIVE VOICE CONVERSION I...

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47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Liberatore, Christopher Gutierrez-Osuna, Ricardo Texas A&M Univ College Stn TX 77843 USA

ISBN: (纸本)9781665405409

We present a dictionary-learning algorithm for reducing the sparse coding residual of an exemplar-based method for native-to-nonnative voice conversion (VC). The proposed algorithm iteratively updates the source and target speaker dictionaries to reduce both the residual and voice conversion error, thereby increasing synthesis quality. We evaluate the method on speech from the ARCTIC and L2-ARCTIC corpora and compare it to a baseline exemplar-based VC algorithm. The proposed algorithm significantly improves synthesis quality to more than double that of the baseline system while using two orders of magnitude fewer atoms. Additionally, the proposed algorithm significantly reduces both the VC error and the residual magnitude. We discuss the implications of the algorithm for broad exemplar-based VC systems.

关键词： sparse coding voice conversion residual exemplar voice conversion dictionary learning

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