IEEE open journal of signal processing最新文献

{"title":"Bilinear Expectation Propagation for Distributed Semi-Blind Joint Channel Estimation and Data Detection in Cell-Free Massive MIMO","authors":"Alexander Karataev;Christian Forsch;Laura Cottatellucci","doi":"10.1109/OJSP.2023.3348343","DOIUrl":"10.1109/OJSP.2023.3348343","url":null,"abstract":"We consider a cell-free massive multiple-input multiple-output (CF-MaMIMO) communication system in the uplink transmission and propose a novel algorithm for blind or semi-blind joint channel estimation and data detection (JCD). We formulate the problem in the framework of bilinear inference and develop a solution based on the expectation propagation (EP) method for both channel estimation and data detection. We propose a new approximation of the joint a posteriori distribution of the channel and data whose representation as a factor graph enables the application of the EP approach using the message-passing technique, local low-complexity computations at the nodes, and an effective modeling of channel-data interplay. The derived algorithm, called bilinear-EP JCD, allows for a distributed implementation among access points (APs) and the central processing unit (CPU) and has polynomial complexity. Our simulation results show that it outperforms other EP-based state-of-the-art polynomial time algorithms.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"284-293"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10378663","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139175602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Detection and Estimation of Gas Sources With Arbitrary Locations Based on Poisson's Equation","authors":"Dmitriy Shutin;Thomas Wiedemann;Patrick Hinsen","doi":"10.1109/OJSP.2023.3344076","DOIUrl":"https://doi.org/10.1109/OJSP.2023.3344076","url":null,"abstract":"Accurate estimation of the number and locations of dispersed material sources is critical for optimal disaster response in Chemical, Biological, Radiological, or Nuclear accidents. This paper introduces a novel approach to Gas Source Localization that uses sparse Bayesian learning adapted to models based on Partial Differential Equations for modeling gas dynamics. Using the method of Green's functions and the adjoint state method, a gradient-based optimization with respect to source locations is derived, allowing superresolving (arbitrary) source locations. By combing the latter with sparse Bayesian learning, a sparse source support can be identified, thus indirectly assessing the number of sources. Simulation results and comparisons with classical sparse estimators for linear models demonstrate the effectiveness of the proposed approach. The proposed sparsity-constrained gas source localization method offers thus a flexible solution for disaster response and robotic exploration in hazardous environments.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"359-373"},"PeriodicalIF":0.0,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10368587","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139573275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Group Conversations in Noisy Environments (GiN) – Multimedia Recordings for Location-Aware Speech Enhancement","authors":"Emilie d'Olne;Alastair H. Moore;Patrick A. Naylor;Jacob Donley;Vladimir Tourbabin;Thomas Lunner","doi":"10.1109/OJSP.2023.3344379","DOIUrl":"https://doi.org/10.1109/OJSP.2023.3344379","url":null,"abstract":"Recent years have seen a growing interest in the use of smart glasses mounted with microphones to solve the cocktail party problem using beamforming techniques or machine learning. Many such approaches could bring substantial advances in hearing aid or Augmented Reality (AR) research. To validate these methods, the EasyCom [Donley et al., 2021] dataset introduced high-quality multi-modal recordings of conversations in noise, including egocentric multi-channel microphone array audio, speech source pose, and headset microphone audio. While providing comprehensive data, EasyCom lacks diversity in the acoustic environments considered and the degree of overlapping speech in conversations. This work therefore presents the Group in Noise (GiN) dataset of over 2 hours of group conversations in noisy environments recorded using binaural microphones and a pair of glasses mounted with 5 microphones. The recordings took place in 3 rooms and contain 6 seated participants as well as a standing facilitator. The data also include close-talking microphone audio and head-pose data for each speaker, an audio channel from a fixed reference microphone, and automatically annotated speaker activity information. A baseline method is used to demonstrate the use of the data for speech enhancement. The dataset is publicly available in d'Olne et al. [2023].","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"374-382"},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10365406","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139573273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sparse Representation With Gaussian Atoms and Its Use in Anomaly Detection","authors":"Denis C. Ilie-Ablachim;Andra Băltoiu;Bogdan Dumitrescu","doi":"10.1109/OJSP.2023.3344313","DOIUrl":"https://doi.org/10.1109/OJSP.2023.3344313","url":null,"abstract":"We propose sparse representation and dictionary learning algorithms for dictionaries whose atoms are characterized by Gaussian probability distributions around some central atoms. This extension of the space covered by the atoms permits a better characterization of localized features of the signals. The representations are computed by trading-off representation error and the probability of the actual atoms used in the representation. We propose two types of algorithms: a greedy one, similar in spirit with Orthogonal Matching Pursuit, and one based on L1-regularization. We apply our new algorithms to unsupervised anomaly detection, where the representation error is used as anomaly score. The flexibility of our approach appears to improve more the representations of the many normal signals than those of the few outliers, at least for an anomaly type called dependency, thus improving the detection quality. Comparison with both standard dictionary learning algorithms and established anomaly detection methods is favorable.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"168-176"},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10365333","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139060324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Smoothing ADMM for Sparse-Penalized Quantile Regression With Non-Convex Penalties","authors":"Reza Mirzaeifard;Naveen K. D. Venkategowda;Vinay Chakravarthi Gogineni;Stefan Werner","doi":"10.1109/OJSP.2023.3344395","DOIUrl":"https://doi.org/10.1109/OJSP.2023.3344395","url":null,"abstract":"This paper investigates quantile regression in the presence of non-convex and non-smooth sparse penalties, such as the minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD). The non-smooth and non-convex nature of these problems often leads to convergence difficulties for many algorithms. While iterative techniques such as coordinate descent and local linear approximation can facilitate convergence, the process is often slow. This sluggish pace is primarily due to the need to run these approximation techniques until full convergence at each step, a requirement we term as a \u0000<italic>secondary convergence iteration</i>\u0000. To accelerate the convergence speed, we employ the alternating direction method of multipliers (ADMM) and introduce a novel single-loop smoothing ADMM algorithm with an increasing penalty parameter, named SIAD, specifically tailored for sparse-penalized quantile regression. We first delve into the convergence properties of the proposed SIAD algorithm and establish the necessary conditions for convergence. Theoretically, we confirm a convergence rate of \u0000<inline-formula><tex-math>$o({k^{-frac{1}{4}}})$</tex-math></inline-formula>\u0000 for the sub-gradient bound of the augmented Lagrangian, where \u0000<inline-formula><tex-math>$k$</tex-math></inline-formula>\u0000 denotes the number of iterations. Subsequently, we provide numerical results to showcase the effectiveness of the SIAD algorithm. Our findings highlight that the SIAD method outperforms existing approaches, providing a faster and more stable solution for sparse-penalized quantile regression.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"213-228"},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10365338","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139090553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"VoicePAT: An Efficient Open-Source Evaluation Toolkit for Voice Privacy Research","authors":"Sarina Meyer;Xiaoxiao Miao;Ngoc Thang Vu","doi":"10.1109/OJSP.2023.3344375","DOIUrl":"https://doi.org/10.1109/OJSP.2023.3344375","url":null,"abstract":"Speaker anonymization is the task of modifying a speech recording such that the original speaker cannot be identified anymore. Since the first Voice Privacy Challenge in 2020, along with the release of a framework, the popularity of this research topic is continually increasing. However, the comparison and combination of different anonymization approaches remains challenging due to the complexity of evaluation and the absence of user-friendly research frameworks. We therefore propose an efficient speaker anonymization and evaluation framework based on a modular and easily extendable structure, almost fully in Python. The framework facilitates the orchestration of several anonymization approaches in parallel and allows for interfacing between different techniques. Furthermore, we propose modifications to common evaluation methods which improves the quality of the evaluation and reduces their computation time by 65 to 95%, depending on the metric. Our code is fully open source.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"257-265"},"PeriodicalIF":0.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10365329","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139109683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic Time Signature Recognition, Tempo Inference, and Beat Tracking Through the Metrogram Transform","authors":"James M. Cozens;Simon J. Godsill","doi":"10.1109/OJSP.2023.3344048","DOIUrl":"https://doi.org/10.1109/OJSP.2023.3344048","url":null,"abstract":"This paper proposes a probabilistic approach for extracting time-varying and irregular time signature information from polyphonic audio extracts, subsequently providing beat and bar line positions given inferred time signature divisions. This is achieved via dynamically evaluating the beat tempo as a function of time through finding an optimal compromise in beat and bar alignment in the time and tempo domains. Time signature divisions are determined based on a new representation, termed the Metrogram, that presents time-varying information regarding rhythmic and metric periodicities in the Tempogram. Our methodology is characterised by its ability to provide a distribution over metric interpretations, offering insights into the diverse ways music can be rhythmically perceived. Results indicate high-level accuracy for a variety of polyphonic extracts containing irregular, complex, irrational, and time-varying time signatures. Accuracy rivalling state-of-the-art methodologies is also reported in a beat tracking task performed on the standard Ballroom Dataset. The paper offers insights into the field of dynamic time signature recognition and beat tracking, offering a valuable and versatile resource for the analysis, composition, and performance of music.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"140-149"},"PeriodicalIF":0.0,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10363392","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139060183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Zero-Shot Visual Sentiment Prediction via Cross-Domain Knowledge Distillation","authors":"Yuya Moroto;Yingrui Ye;Keisuke Maeda;Takahiro Ogawa;Miki Haseyama","doi":"10.1109/OJSP.2023.3344079","DOIUrl":"https://doi.org/10.1109/OJSP.2023.3344079","url":null,"abstract":"There are various sentiment theories for categorizing human sentiments into several discrete sentiment categories, which means that the theory used for training sentiment prediction methods does not always match that used in the test phase. As a solution to this problem, zero-shot visual sentiment prediction methods have been proposed to predict unseen sentiments for which no images are available in the training phase. However, the training of these previous zero-shot methods relies on a single sentiment theory, which limits their ability to handle sentiments from other theories. Thus, this article proposes a more robust zero-shot visual sentiment prediction method that can handle cross-domain sentiments defined in different sentiment theories. Specifically, by focusing on the fact that sentiments are abstract concepts common to humans regardless of whether their theories are different, we incorporate knowledge distillation into our method to construct a teacher–student model that can train the implicit relationships between sentiments defined in different sentiment theories. Furthermore, to enhance sentiment discrimination capability and strengthen the implicit relationships between sentiments, we introduce a novel sentiment loss between the teacher and student models. In this way, our model becomes robust to unseen sentiments by exploiting the implicit relationships between sentiments. The contributions of this article are the introduction of knowledge distillation and a novel sentiment loss between the teacher and student models for zero-shot visual sentiment prediction, and improved performance of zero-shot visual sentiment prediction. Experiments on several open datasets demonstrate the effectiveness of the proposed method.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"177-185"},"PeriodicalIF":0.0,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10363382","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139060185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reverse Ordering Techniques for Attention-Based Channel Prediction","authors":"Valentina Rizzello;Benedikt Böck;Michael Joham;Wolfgang Utschick","doi":"10.1109/OJSP.2023.3344024","DOIUrl":"https://doi.org/10.1109/OJSP.2023.3344024","url":null,"abstract":"Channel state information (CSI) is crucial for enhancing the performance of wireless systems by allowing to adjust the transmission strategies based on the current channel conditions. However, obtaining precise CSI is difficult because of the fast-changing channel conditions caused by multi-path fading. An inaccurate CSI hinders the performance of various adaptive wireless systems, highlighting the need for channel prediction techniques to effectively mitigate the drawbacks of outdated CSI. Conventional methods typically depend on assumptions regarding user velocity or require knowledge of the Doppler frequency. In contrast to existing approaches, we aim for a more robust and practical solution by training neural networks without making any assumptions about user velocity, relying solely on noisy channel observations during training. Specifically, we adapt both the sequence-to-sequence with attention (Seq2Seq-attn) and transformer models for channel prediction. Additionally, a new technique called reverse positional encoding is introduced in the transformer model to improve the robustness of the model against varying sequence lengths. Similarly, the encoder outputs of the Seq2Seq-attn model are reversed prior to the application of attention mechanisms. By means of simulations, we show that these proposed techniques enable the models to effectively capture relationships within sequences of channel snapshots without increasing the complexity. Importantly, this capability remains robust across varying sequence lengths, representing a substantial improvement over existing methodologies.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"248-256"},"PeriodicalIF":0.0,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10363354","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139109682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distributed Adaptive Learning Under Communication Constraints","authors":"Marco Carpentiero;Vincenzo Matta;Ali H. Sayed","doi":"10.1109/OJSP.2023.3344052","DOIUrl":"https://doi.org/10.1109/OJSP.2023.3344052","url":null,"abstract":"We consider a network of agents that must solve an online optimization problem from continual observation of \u0000<italic>streaming</i>\u0000 data. To this end, the agents implement a distributed \u0000<italic>cooperative</i>\u0000 strategy where each agent is allowed to perform \u0000<italic>local</i>\u0000 exchange of information with its neighbors. In order to cope with communication constraints, the exchanged information must be compressed to reduce the communication load. We propose a distributed diffusion strategy nicknamed as ACTC (Adapt-Compress-Then-Combine), which implements the following three operations: adaptation, where each agent performs an individual stochastic-gradient update; compression, which leverages a recently introduced class of \u0000<italic>stochastic compression operators</i>\u0000; and combination, where each agent combines the \u0000<italic>compressed</i>\u0000 updates received from its neighbors. The main elements of novelty of this work are as follows: \u0000<inline-formula><tex-math>$i)$</tex-math></inline-formula>\u0000 \u0000<italic>adaptive</i>\u0000 strategies, where constant (as opposed to diminishing) step-sizes are critical to infuse the agents with the ability of responding in real time to nonstationary variations in the observed model; \u0000<inline-formula><tex-math>$ii)$</tex-math></inline-formula>\u0000 \u0000<italic>directed</i>\u0000, i.e., non-symmetric combination policies, which allow us to enhance the role played by the network topology in the learning performance; \u0000<inline-formula><tex-math>$iii)$</tex-math></inline-formula>\u0000 \u0000<italic>global strong convexity</i>\u0000, a condition under which the individual agents might feature even non-convex cost functions. Under this demanding setting, we establish that the iterates of the ACTC strategy fluctuate around the exact global optimizer with a mean-square-deviation on the order of the step-size, achieving remarkable savings of communication resources. Comparison against up-to-date learning strategies with compressed data highlights the benefits of the proposed solution.","PeriodicalId":73300,"journal":{"name":"IEEE open journal of signal processing","volume":"5 ","pages":"321-358"},"PeriodicalIF":0.0,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10363388","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139573274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}