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Approved: August 2024
IEEE Signal Processing Society (SPS) maintains one set of technical topic areas across all technical activities, often referred to as the Unified EDICS list. This list is used as a basis for all topics under each of the technical committees, flagship conferences, and solely sponsored journals. This list may be updated once a year each April, with agreement by both the Editors-in-Chief (EICs) and Technical Committee (TC) Chairs responsible for the topic area, and approved by the SPS Executive Committee. Changes may not be requested or made outside of this annual cycle. For more information, please contact sp-info@ieee.org.
Level - 1 | Level - 2 | Level - 3 | Journal | TC | ICASSP | ICIP | ||
---|---|---|---|---|---|---|
Signal Processing Theory and Methods | T-SP/T-SIPN | SPTM | ✅ | |||
Digital signal processing | T-SP | SPTM | ||||
Signal and system modeling | T-SP | SPTM | ✅ | |||
Sampling theory | T-SP | SPTM | ✅ | |||
Transforms | T-SP | SPTM | ✅ | |||
Filtering | T-SP | SPTM | ✅ | |||
Multidimensional signal processing | T-SP | SPTM | ||||
Multirate signal processing and filter banks | T-SP | SPTM | ||||
Time-frequency and multiresolution analysis | T-SP | SPTM | ✅ | |||
Quantization | T-SP | SPTM | ||||
Fast algorithms | T-SP | SPTM | ||||
Statistical signal processing | T-SP | SPTM | ||||
Statistical signal modeling, identification, and analysis | T-SP | SPTM | ||||
Spectral analysis and filtering | T-SP | SPTM | ||||
Estimation | T-SP | SPTM | ✅ | |||
Detection and classification | T-SP | SPTM | ✅ | |||
Bayesian signal processing | T-SP | SPTM | ✅ | |||
Robust signal processing | T-SP | SPTM | ||||
Sparse and low-dimensional signal recovery | T-SP | SPTM | ✅ | |||
Nonstationary statistical signal processing | T-SP | SPTM | ||||
Random matrix methods | T-SP | SPTM | ||||
Performance analysis and bounds | T-SP | SPTM | ||||
Matrix and tensor methods | T-SP | SPTM | ||||
Matrix and tensor factorization and completion | T-SP | SPTM | ✅ | |||
Sparse and non-negative matrices and tensors | T-SP | SPTM | ✅ | |||
Source separation | T-SP | SPTM | ✅ | |||
Independent component analysis | T-SP | SPTM | ✅ | |||
Subspace and manifold learning | T-SP | SPTM | ✅ | |||
Tensor-based signal processing | T-SP | SPTM | ✅ | |||
Adaptive signal processing | T-SP | SPTM | ✅ | |||
Adaptive filter analysis and design | T-SP | SPTM | ||||
Frequency domain and subband adaptive filtering | T-SP | SPTM | ||||
Fast algorithms | T-SP | SPTM | ||||
Tracking | T-SP | SPTM | ✅ | |||
Optimization methods | T-SP | SPTM | ✅ | |||
Convex optimization | T-SP | SPTM | ||||
Non-convex optimization | T-SP | SPTM | ||||
Distributed optimization | T-SP | SPTM | ✅ | |||
Sparse optimization | T-SP | SPTM | ||||
Graph signal processing | T-SP/T-SIPN | SPTM | ✅ | |||
Graph signal modeling and analysis | T-SP/T-SIPN | SPTM | ||||
Graph modeling and topology identification | T-SP/T-SIPN | SPTM | ||||
Sampling theory for graphs | T-SP/T-SIPN | SPTM | ||||
Graph filtering and transforms | T-SP/T-SIPN | SPTM | ||||
Stochastic graph signal processing | T-SP/T-SIPN | SPTM | ||||
Graph-time processing | T-SP/T-SIPN | SPTM | ||||
Topological signal processing | T-SP/T-SIPN | SPTM | ||||
Distributed signal processing theory and methods | T-SP/T-SIPN | SPTM | ✅ | |||
Distributed estimation | T-SP/T-SIPN | SPTM | ||||
Distributed detection and classification | T-SP/T-SIPN | SPTM | ||||
Distributed adaptation | T-SP/T-SIPN | SPTM | ||||
Distributed optimization | T-SP/T-SIPN | SPTM | ✅ | |||
Applications and other topics of signal processing theory and methods | T-SP/T-SIPN | SPTM | ✅ |
Level - 1 | Level - 2 | Level - 3 | Journal | TC | ICASSP | ICIP | ||
---|---|---|---|---|---|---|
Machine Learning in Signal Processing | T-SP/T-SIPN | MLSP | ✅ | ✅ | ||
Machine learning paradigms | T-SP | MLSP | ||||
Supervised and semi-supervised learning | T-SP | MLSP | ✅ | |||
Unsupervised learning | T-SP | MLSP | ✅ | |||
Self-supervised learning | T-SP | MLSP | ✅ | |||
Online learning | T-SP | MLSP | ||||
Representation learning | T-SP | MLSP | ||||
Regression and classification | T-SP | MLSP | ||||
Pattern recognition and clustering | T-SP | MLSP | ✅ | |||
Performance analysis and bounds | T-SP | MLSP | ✅ | |||
Reinforcement learning | T-SP | MLSP | ✅ | |||
Deep reinforcement learning | T-SP | MLSP | ||||
Policy optimization and policy gradient | T-SP | MLSP | ||||
Q-learning | T-SP | MLSP | ||||
Inverse reinforcement learning | T-SP | MLSP | ||||
Exploration and exploitation | T-SP | MLSP | ||||
Deep learning | T-SP | MLSP | ||||
Deep learning models | T-SP | MLSP | ✅ | |||
Deep learning training methods | T-SP | MLSP | ✅ | |||
Deep generative models | T-SP | MLSP | ✅ | |||
Deep learning fairness and privacy | T-SP | MLSP | ✅ | |||
Transfer learning and meta-learning | T-SP | MLSP | ✅ | |||
Distributed and federated learning | T-SIPN | MLSP | ✅ | |||
Learning architectures and algorithms | T-SIPN | MLSP | ||||
Security and privacy | T-SIPN | MLSP | ||||
Scalability and resource optimization | T-SIPN | MLSP | ||||
Robustness and fault tolerance | T-SIPN | MLSP | ||||
Dynamic and heterogeneous environments | T-SIPN | MLSP | ||||
Communication and synchronisation issues | T-SIPN | MLSP | ||||
Graph neural networks | T-SIPN | MLSP | ✅ | |||
Graph convolutional neural network | T-SIPN | MLSP | ||||
Graph attention network | T-SIPN | MLSP | ||||
Graph sequence neural network | T-SIPN | MLSP | ||||
Topological deep learning | T-SIPN | MLSP | ||||
Emerging machine learning topics | T-SP | MLSP | ||||
Explainable and interpretable machine learning | T-SP | MLSP | ✅ | |||
Adversarial machine learning | T-SP | MLSP | ✅ | |||
Robust and trustworthy machine learning | T-SP | MLSP | ✅ | |||
Causal machine learning | T-SP | MLSP | ||||
Sustainable machine learning | T-SP | MLSP | ✅ | |||
Quantum machine learning | T-SP | MLSP | ✅ | |||
Conventional machine learning | T-SP | MLSP | ||||
Graphical and kernel methods | T-SP | MLSP | ✅ | |||
Dictionary learning | T-SP | MLSP | ✅ | |||
Information theoretic learning | T-SP | MLSP | ✅ | |||
Bayesian machine learning | T-SP | MLSP | ✅ | |||
Sequential learning | T-SP | MLSP | ✅ | |||
Sparsity-aware learning | T-SP | MLSP | ✅ | |||
Tiny and efficient machine learning | T-SP | MLSP | ||||
Feature extraction and selection | T-SP | MLSP | ✅ | |||
Applications and other topics of machine learning | T-SP | MLSP | ✅ | |||
Machine learning applications for big data | T-SP | MLSP | ✅ | |||
Machine learning applications for wireless networks | T-SP | MLSP | ✅ | |||
Machine learning applications for communications | T-SP | MLSP | ✅ | |||
Machine learning applications for image and video processing | T-SP | MLSP | ✅ | ✅ | ||
Machine learning applications for biomedical signal and image processing | T-SP | MLSP | ✅ | |||
Machine learning applications for information forensics and security | T-SP | MLSP | ✅ | |||
Machine learning applications for speech, music, and audio processing | T-SP | MLSP | ✅ | |||
Machine learning applications for time series analysis | T-SP | MLSP | ✅ | |||
Machine learning applications for multimodal data | T-SP | MLSP | ✅ | ✅ | ||
Machine learning applications for sciences | T-SP | MLSP | ||||
Other applications of machine learning | T-SP | MLSP | ✅ |
Level - 1 | Level - 2 | Level - 3 | Journal | TC | ICASSP | ICIP | ||
---|---|---|---|---|---|---|
Signal Processing for Sensing and Communication | T-SP/T-SIPN | SPCOM/SAM | ✅ | |||
Array signal processing | T-SP | SAM | ||||
Beamforming and source separation | T-SP | SAM | ✅ | |||
Direction of arrival estimation and source localization | T-SP | SAM | ✅ | |||
Array calibration | T-SP | SAM | ✅ | |||
Tracking | T-SP | SAM | ✅ | |||
Performance analysis and bounds | T-SP | SAM | ✅ | |||
MIMO and massive MIMO array processing | T-SP | SAM | ✅ | |||
mmWave, UWB and THz array processing | T-SP | SAM | ||||
Interference management | T-SP | SAM | ||||
Signal processing for communication | T-SP | SPCOM | ||||
Signal modulation and encoding | T-SP | SPCOM | ✅ | |||
Signal detection, estimation, demodulation and decoding | T-SP | SPCOM | ✅ | |||
Channel modeling and estimation | T-SP | SPCOM | ✅ | |||
Machine learning for communications | T-SP | SPCOM | ✅ | |||
Information theory | T-SP | SPCOM | ✅ | |||
Physical layer security | T-SP | SPCOM | ✅ | |||
MIMO and massive MIMO communication | T-SP | SPCOM | ✅ | |||
mmWave, UWB and THz communication | T-SP | SPCOM | ||||
Interference management in communications / networked systems | T-SP | SPCOM | ||||
Low latency communication | T-SP | SPCOM | ✅ | |||
Energy aware communication | T-SP | SPCOM | ✅ | |||
Signal processing for networks and distributed systems | T-SIPN | SPCOM | ✅ | |||
Network modeling and identification | T-SIPN | SPCOM | ||||
Network resource management | T-SIPN | SPCOM | ✅ | |||
Network design | T-SIPN | SPCOM | ||||
Network security | T-SIPN | SPCOM | ||||
Decision making over networks | T-SIPN | SPCOM | ||||
Edge, sensor and ad-hoc networks | T-SIPN | SPCOM | ✅ | |||
Cooperative networking / cognitive radio | T-SIPN | SPCOM | ✅ | |||
Internet of things | T-SIPN | SPCOM | ||||
Distributed processing | T-SIPN | SPCOM | ✅ | |||
Modeling and analysis of distributed systems | T-SIPN | SPCOM | ||||
Distributed detection and estimation | T-SIPN | SPCOM | ||||
Distributed information processing | T-SIPN | SPCOM | ||||
Distributed localization and target tracking | T-SIPN | SPCOM | ||||
Machine learning over distributed networks | T-SIPN | SPCOM | ✅ | |||
Integrated sensing and communication | T-SP | SPCOM/SAM | ✅ | |||
Theory and performance limits for ISAC | T-SP | SPCOM/SAM | ||||
Signalling and waveform design for ISAC | T-SP | SPCOM/SAM | ||||
Detection, estimation and demodulation for ISAC | T-SP | SPCOM/SAM | ||||
MIMO and distributed MIMO architectures for ISAC | T-SP | SPCOM/SAM | ||||
Security and privacy issues in ISAC | T-SP | SPCOM/SAM | ||||
Hardware-/energy-efficient technologies, and experimentation of ISAC systems | T-SP | SPCOM/SAM | ||||
Remote sensing, radar and sonar signal processing | T-SP | SAM | ✅ | |||
Space-time adaptive methods | T-SP | SAM | ||||
Synthetic aperture sensing | T-SP | SAM | ||||
MIMO radar and waveform design | T-SP | SAM | ✅ | |||
Distributed processing | T-SP | SAM | ||||
Cognitive radar | T-SP | SAM | ||||
Passive radar | T-SP | SAM | ||||
System resource management | T-SP | SAM | ||||
Sonar and underwater signal processing | T-SP | SAM | ||||
Applications and other topics in signal processing for sensing and communication | T-SP | SPCOM/SAM | ||||
Sensor arrays for medical signal and image processing | T-SP | SAM | ✅ | |||
Acoustic and microphone array processing | T-SP | SAM | ✅ | |||
Geophysical and seismic signal processing | T-SP | SAM | ✅ | |||
Non-wave based array processing | T-SP | SAM | ✅ | |||
Non-terrestrial communications | T-SP | SPCOM | ✅ | |||
Optical wireless communication | T-SP | SPCOM | ✅ | |||
Quantum communication | T-SP | SPCOM | ✅ | |||
Intelligent surfaces | T-SP | SPCOM/SAM | ✅ | |||
Other topics in signal processing for sensing and communication | T-SP | SPCOM/SAM | ✅ |
Level - 1 | Level - 2 | Level - 3 | Journal | TC | ICASSP | ICIP | ||
---|---|---|---|---|---|---|
Biomedical Signal and Image Processing | T-SP/T-IP | BISP | ✅ | ✅ | ||
Medical imaging | T-IP | BISP | ✅ | |||
Medical image formation, reconstruction and restoration | T-IP | BISP | ✅ | |||
Medical image analysis | T-IP | BISP | ✅ | ✅ | ||
Multimodal medical image fusion and analysis | T-IP | BISP | ✅ | |||
Biological imaging | T-IP | BISP | ✅ | |||
Biological image formation, reconstruction and restoration | T-IP | BISP | ✅ | |||
Biological image analysis | T-IP | BISP | ✅ | ✅ | ||
Biomedical signal processing | T-SP | BISP | ||||
Physiological and wearable signal processing | T-SP | BISP | ✅ | |||
Neural signals | T-SP | BISP | ✅ | |||
Brain/human-computer interfaces | T-SP | BISP | ✅ | ✅ | ||
Bioinformatics | T-SP | BISP | ✅ | |||
Applications and emerging methods in biomedical image and signal processing | T-SP | BISP | ✅ | ✅ |
Level - 1 | Level - 2 | Level - 3 | Journal | TC | ICASSP | ICIP | ||
---|---|---|---|---|---|---|
Image, Video and Multidimensional Signal Processing | T-IP | IVMSP | ✅ | ✅ | ||
Image and video sensing, modeling, and representation | T-IP | IVMSP | ✅ | |||
Image and video sensing and acquisition | T-IP | IVMSP | ✅ | ✅ | ||
Statistical-model based methods for image and video | T-IP | IVMSP | ✅ | ✅ | ||
Structural-model based methods for image and video | T-IP | IVMSP | ✅ | ✅ | ||
Image and video representation | T-IP | IVMSP | ✅ | ✅ | ||
Perception and quality models for images and video | T-IP | IVMSP | ✅ | ✅ | ||
Machine learning for image and video sensing, modeling and representation | T-IP | IVMSP | ✅ | |||
Image and video processing techniques | T-IP | IVMSP | ✅ | |||
Linear and nonlinear filtering of images and video | T-IP | IVMSP | ✅ | |||
Partial differential equation based processing of images and video | T-IP | IVMSP | ✅ | |||
Multiresolution processing of images and video | T-IP | IVMSP | ✅ | |||
Restoration and enhancement of images and video | T-IP | IVMSP | ✅ | |||
Interpolation, super-resolution, and mosaicing of images and video | T-IP | IVMSP | ✅ | |||
Biomedical and biological image processing | T-IP | IVMSP | ✅ | ✅ | ||
Machine learning for image and video processing | T-IP | IVMSP | ✅ | ✅ | ||
Image and video communications | T-IP | IVMSP | ✅ | |||
Image and video coding | T-IP | IVMSP | ✅ | ✅ | ||
Imaging and video communication networks | T-IP | IVMSP | ✅ | ✅ | ||
Image and video processing for watermarking and security | T-IP | IVMSP | ✅ | ✅ | ||
Image and video multimedia communications | T-IP | IVMSP | ✅ | ✅ | ||
Machine learning for image and video communication | T-IP | IVMSP | ✅ | ✅ | ||
Image and video analysis, synthesis, and retrieval | T-IP | IVMSP | ✅ | |||
Image and video content analysis | T-IP | IVMSP | ✅ | ✅ | ||
Image and video mid level analysis | T-IP | IVMSP | ✅ | |||
Image and video interpretation and understanding | T-IP | IVMSP | ✅ | |||
Image and video biometric analysis | T-IP | IVMSP | ✅ | |||
Image and video storage and retrieval | T-IP | IVMSP | ✅ | ✅ | ||
Image and video synthesis, rendering, and visualization | T-IP | IVMSP | ✅ | ✅ | ||
Region, boundary, texture and shape analysis | T-IP | IVMSP | ✅ | |||
Machine learning for image and video analysis, synthesis, and retrieval | T-IP | IVMSP | ✅ | |||
Three-dimensional image and video analysis and processing | T-IP | IVMSP | ✅ | ✅ | ||
Light-field image processing and compression | T-IP | IVMSP | ✅ | |||
3D image and video analysis and compression | T-IP | IVMSP | ✅ | |||
3D video processing | T-IP | IVMSP | ✅ | |||
Stereoscopic and multiview processing, display and coding | T-IP | IVMSP | ✅ | ✅ | ||
Point cloud processing | T-IP | IVMSP | ✅ | |||
Image and video augmented and virtual reality | T-IP | IVMSP | ✅ | |||
Machine learning for 3D image and video processing | T-IP | IVMSP | ✅ | |||
Electronic imaging | T-IP | IVMSP | ✅ | |||
Image scanning and capture | T-IP | IVMSP | ✅ | ✅ | ||
Color and multispectral imaging | T-IP | IVMSP | ✅ | ✅ | ||
Scanned document analysis, processing, and coding | T-IP | IVMSP | ✅ | ✅ | ||
Hardware and software systems for image and video processing | T-IP | IVMSP | ✅ | ✅ | ||
Machine learning for electronic imaging | T-IP | IVMSP | ✅ | |||
Applications and other topics in image, video and multidimensional signal processing | T-IP | IVMSP | ✅ | ✅ | ||
Remote sensing images | T-IP | IVMSP |
Level - 1 | Level - 2 | Level - 3 | Journal | TC | ICASSP | ICIP | ||
---|---|---|---|---|---|---|
Computational Imaging | T-CI | CI | ✅ | ✅ | ||
Computational imaging methods and models | T-CI | CI | ✅ | ✅ | ||
Sparse, low-rank, and low-dimensional models for computational imaging | T-CI | CI | ✅ | ✅ | ||
Statistical and graphical image models | T-CI | CI | ✅ | |||
Machine learning-based methods for computational imaging | T-CI | CI | ✅ | ✅ | ||
Optimization-based inversion methods | T-CI | CI | ✅ | |||
Multi-image methods and sensor fusion | T-CI | CI | ✅ | |||
Superresolution and inpainting methods for inverse problems | T-CI | CI | ✅ | |||
Performance assessment and uncertainty quantification for computational imaging | T-CI | CI | ✅ | |||
Novel regularization methods | T-CI | CI | ✅ | |||
Other computational imaging methods and models | T-CI | CI | ✅ | |||
Computational imaging modalities | T-CI | CI | ✅ | ✅ | ||
Computational photography | T-CI | CI | ✅ | ✅ | ||
Microscopic and nanoscopic imaging | T-CI | CI | ✅ | |||
Spectral imaging | T-CI | CI | ✅ | |||
Tomographic imaging | T-CI | CI | ✅ | |||
Magnetic resonance imaging | T-CI | CI | ✅ | |||
Acoustic and ultrasound imaging | T-CI | CI | ✅ | |||
Radar/microwave and radio imaging | T-CI | CI | ✅ | |||
Lidar imaging | T-CI | CI | ✅ | |||
Seismic imaging | T-CI | CI | ✅ | |||
Coherent, holographic, and speckle imaging | T-CI | CI | ✅ | |||
Other/novel computational imaging modalities | T-CI | CI | ✅ | |||
Computational imaging hardware and algorithms | T-CI | CI | ✅ | ✅ | ||
High-performance computing for computational imaging | T-CI | CI | ✅ | |||
Fast algorithms for computational imaging | T-CI | CI | ✅ | |||
Integrated hardware and algorithm design | T-CI | CI | ✅ | |||
Computational imaging with novel sensors and acquisition methods | T-CI | CI | ✅ | |||
Applications and other topics in computational imaging | T-CI | CI | ✅ | ✅ |
Level - 1 | Level - 2 | Level - 3 | Journal | TC | ICASSP | ICIP | ||
---|---|---|---|---|---|---|
Multimedia Signal Processing | T-MM | MMSP | ✅ | |||
Multimedia creation and synthesis | T-MM | MMSP | ||||
Multimedia acquisition and sensor technology | T-MM | MMSP | ||||
Multimedia synthesis and rendering | T-MM | MMSP | ✅ | |||
Human centric multimedia synthesis and generation | T-MM | MMSP | ||||
Object centric multimedia synthesis and generation | T-MM | MMSP | ||||
Multimedia architecture design and systems | T-MM | MMSP | ||||
Display technology for multimedia | T-MM | MMSP | ||||
Large-scale multimedia systems and benchmarking | T-MM | MMSP | ||||
Mobile media technology and systems | T-MM | MMSP | ||||
System design and optimization | T-MM | MMSP | ||||
Architectures and design techniques | T-MM | MMSP | ||||
Frugal and green multimedia | T-MM | MMSP | ✅ | |||
Multi-modal processing, analysis and synthesis | T-MM | MMSP | ||||
Multi-modal signal processing and analysis | T-MM | MMSP | ✅ | |||
Machine/deep learning methodologies for multimedia | T-MM | MMSP | ✅ | |||
Generative/large multi-modal models | T-MM | MMSP | ✅ | |||
Distributed multimedia processing and Internet-of-Things | T-MM | MMSP | ||||
Multimedia understanding | T-MM | MMSP | ✅ | |||
Multimedia compression, transmission and security | T-MM | MMSP | ✅ | |||
Multimedia compression, coding, conversion, and transcoding | T-MM | MMSP | ||||
Multimedia communications and streaming | T-MM | MMSP | ||||
Learning-based multimedia compression | T-MM | MMSP | ||||
Multimedia over networks | T-MM | MMSP | ||||
Multimedia security and watermarking | T-MM | MMSP | ||||
Video surveillance and semantic analysis | T-MM | MMSP | ||||
Multimedia standardization | T-MM | MMSP | ||||
Media and data compression standardization | T-MM | MMSP | ||||
Media transmission and communication standardization | T-MM | MMSP | ||||
Media description and representation standardization | T-MM | MMSP | ||||
Media display standards | T-MM | MMSP | ||||
Metaverse media processing standardization | T-MM | MMSP | ||||
Multimedia environments and user experience | T-MM | MMSP | ||||
Human-centric multimedia and human-machine interaction | T-MM | MMSP | ||||
Immersive and 3D multimedia processing and coding | T-MM | MMSP | ✅ | |||
Quality of experience | T-MM | MMSP | ✅ | |||
Audio-visual-haptic environments | T-MM | MMSP | ||||
Multimodal telepresence and collaboration | T-MM | MMSP | ||||
Multimedia information retrieval and datasets | T-MM | MMSP | ✅ | |||
Multimedia search and retrieval | T-MM | MMSP | ||||
Multimedia datasets | T-MM | MMSP | ||||
Knowledge and semantics modeling for multimedia databases | T-MM | MMSP | ||||
Media preservation | T-MM | MMSP | ||||
Validation, quality assessment and corpora for media search | T-MM | MMSP | ||||
Social and web multimedia | T-MM | MMSP | ||||
Applications in multimedia (healthcare, education, art, distributed multimedia, etc.) | T-MM | MMSP | ✅ | |||
Multimedia in healthcare, education, art, and social sciences | T-MM | MMSP | ||||
Multimedia perception and processing for autonomous systems | T-MM | MMSP | ✅ | |||
Other multimedia areas | T-MM | MMSP |
Level - 1 | Level - 2 | Level - 3 | Journal | TC | ICASSP | ICIP | ||
---|---|---|---|---|---|---|
Information Forensics and Security | T-IFS | IFS | ✅ | ✅ | ||
Applied cryptography | T-IFS | IFS | ✅ | |||
Secure computation using homomorphic encryption | T-IFS | IFS | ||||
Secure computation using secret sharing | T-IFS | IFS | ||||
Multimedia encryption | T-IFS | IFS | ||||
Searchable encryption | T-IFS | IFS | ||||
Watermarking and data hiding | T-IFS | IFS | ✅ | ✅ | ||
Theoretical models | T-IFS | IFS | ||||
Steganography and steganalysis | T-IFS | IFS | ||||
Anonymization and data privacy | T-IFS | IFS | ✅ | |||
Privacy protection | T-IFS | IFS | ||||
Privacy attacks | T-IFS | IFS | ||||
Application and systems | T-IFS | IFS | ||||
Differential privacy | T-IFS | IFS | ||||
Multimedia forensics | T-IFS | IFS | ✅ | ✅ | ||
Text fingerprinting | T-IFS | IFS | ||||
Audio forensics | T-IFS | IFS | ||||
Image forensics | T-IFS | IFS | ||||
Video forensics | T-IFS | IFS | ||||
Multimedia content hash functions | T-IFS | IFS | ✅ | |||
Multimedia security analysis and benchmarking | T-IFS | IFS | ||||
Multimedia content authentication and plagiarism detection | T-IFS | IFS | ||||
Machine learning for information forensics and security | T-IFS | IFS | ✅ | ✅ | ||
Adversarial machine learning | T-IFS | IFS | ✅ | |||
AI for security and forensics | T-IFS | IFS | ||||
Security and forensics for AI | T-IFS | IFS | ||||
Biometrics | T-IFS | IFS | ✅ | ✅ | ||
Iris/ocular biometrics | T-IFS | IFS | ||||
Face biometrics | T-IFS | IFS | ||||
Other biometrics | T-IFS | IFS | ||||
Biometrics security and privacy | T-IFS | IFS | ||||
Biometric modalities | T-IFS | IFS | ||||
Cybersecurity | T-IFS | IFS | ✅ | |||
Hardware security | T-IFS | IFS | ✅ | |||
Network security | T-IFS | IFS | ✅ | |||
System security | T-IFS | IFS | ✅ | |||
Communication and information theoretic security | T-IFS | IFS | ✅ | |||
Surveillance | T-IFS | IFS | ✅ | ✅ | ||
Privacy in surveillance | T-IFS | IFS | ||||
Object and event detection and recognition | T-IFS | IFS | ||||
Applications and other topics in forensics and security | T-IFS | IFS | ✅ | ✅ |
Level - 1 | Level - 2 | Level - 3 | Journal | TC | ICASSP | ICIP | ||
---|---|---|---|---|---|---|
Audio and Acoustic Signal Processing | T-ASLP | AASP | ✅ | |||
Audio signal processing | T-ASLP | AASP | ||||
Signal enhancement, restoration, and extraction | T-ASLP | AASP | ✅ | |||
Audio and speech source separation | T-ASLP | AASP | ✅ | |||
Audio and speech coding, transmission, and representations | T-ASLP | AASP | ✅ | |||
Audio and speech quality and intelligibility measures | T-ASLP | AASP | ✅ | |||
Auditory modeling and hearing instruments | T-ASLP | AASP | ✅ | |||
System identification and dereverberation | T-ASLP | AASP | ✅ | |||
Acoustic sensor array processing | T-ASLP | AASP | ✅ | |||
Fundamental theory and algorithms for audio and acoustic signal processing | T-ASLP | AASP | ✅ | |||
Acoustic scenes and events | T-ASLP | AASP | ||||
Audio captioning, retrieval, and understanding | T-ASLP | AASP | ✅ | |||
Sound event and anomaly detection and sound scene classification | T-ASLP | AASP | ✅ | |||
Sound generation and synthesis | T-ASLP | AASP | ✅ | |||
Acoustic environment processing | T-ASLP | AASP | ||||
Modeling, analysis, and synthesis of acoustic environments | T-ASLP | AASP | ✅ | |||
Spatial audio recording and reproduction | T-ASLP | AASP | ✅ | |||
Active noise control; acoustic echo and feedback cancellation | T-ASLP | AASP | ✅ | |||
Music analysis, processing, and generation | T-ASLP | AASP | ||||
Music analysis | T-ASLP | AASP | ✅ | |||
Music signal processing, production, and separation | T-ASLP | AASP | ✅ | |||
Audio- and symbolic-domain music generation and content creation | T-ASLP | AASP | ✅ | |||
Applications and other topics in audio and acoustic signal processing | T-ASLP | AASP | ||||
Bioacoustics and medical acoustics | T-ASLP | AASP | ✅ | |||
Audio security | T-ASLP | AASP | ✅ | |||
Audio for video and multimedia | T-ASLP | AASP | ✅ | |||
Data and open source for audio and acoustic signal processing | T-ASLP | AASP | ✅ |
Level - 1 | Level - 2 | Level - 3 | Journal | TC | ICASSP | ICIP | ||
---|---|---|---|---|---|---|
Speech and Language Processing | T-ASLP | SLP | ✅ | |||
Human language processing | T-ASLP | SLP | ||||
Discourse and dialog | T-ASLP | SLP | ✅ | |||
Language understanding and computational semantics | T-ASLP | SLP | ✅ | |||
Spoken document retrieval and summarization | T-ASLP | SLP | ✅ | |||
Segmentation, tagging, and parsing of language | T-ASLP | SLP | ✅ | |||
Summarization, retrieval and language learning | T-ASLP | SLP | ✅ | |||
Machine Learning for natural language processing | T-ASLP | SLP | ✅ | |||
Generation in natural language processing | T-ASLP | SLP | ✅ | |||
Question answering | T-ASLP | SLP | ✅ | |||
Multi-modal/cross-modal speech and language processing | T-ASLP | SLP | ✅ | |||
Audio-visual speech/intent recognition | T-ASLP | SLP | ||||
Cross-modal processing of language | T-ASLP | SLP | ||||
Multimodal processing of language | T-ASLP | SLP | ||||
Multimodal processing of speech | T-ASLP | SLP | ||||
Speaker recognition, identification and verification | T-ASLP | SLP | ||||
Speaker diarization and identification | T-ASLP | SLP | ✅ | |||
Speaker verification | T-ASLP | SLP | ✅ | |||
Speaker anti-spoofing | T-ASLP | SLP | ✅ | |||
Speech enhancement and extraction | T-ASLP | SLP | ✅ | |||
Self-supervised learning for speech processing | T-ASLP | SLP | ||||
Speech analysis - pitch and spectrum | T-ASLP | SLP | ||||
Speech analysis and enhancement | T-ASLP | SLP | ||||
Speech coding and compression | T-ASLP | SLP | ||||
Speech separation and extraction | T-ASLP | SLP | ||||
Voice, speech and language disorder | T-ASLP | SLP | ||||
Speech event detection | T-ASLP | SLP | ✅ | |||
Keyword and voice activity detection | T-ASLP | SLP | ||||
Non-speech acoustic event detection | T-ASLP | SLP | ||||
Speaker change detection | T-ASLP | SLP | ||||
Speech emotion recognition | T-ASLP | SLP | ✅ | |||
Speech generation | T-ASLP | SLP | ||||
Speaker anonymization | T-ASLP | SLP | ||||
Speech/singing voice conversion and cloning | T-ASLP | SLP | ✅ | |||
Text-to-speech generation | T-ASLP | SLP | ✅ | |||
Neural vocoder and codec | T-ASLP | SLP | ✅ | |||
Audio/music/singing voice generation | T-ASLP | SLP | ✅ | |||
Watermarking and anti-spoofing | T-ASLP | SLP | ✅ | |||
Speech processing resources | T-ASLP | SLP | ✅ | |||
Data sets for speech processing | T-ASLP | SLP | ||||
Language resources, metrics and systems | T-ASLP | SLP | ||||
Resource constrained speech-to-text | T-ASLP | SLP | ||||
Speech recognition | T-ASLP | SLP | ✅ | |||
Context and language modeling for speech recognition | T-ASLP | SLP | ||||
Language identification | T-ASLP | SLP | ||||
Multilingual speech recognition and identification | T-ASLP | SLP | ✅ | |||
Multi-talker speech recognition | T-ASLP | SLP | ✅ | |||
Natural language processing for speech recognition | T-ASLP | SLP | ||||
New algorithms/approaches for speech-to-text | T-ASLP | SLP | ||||
Speech modeling for speech recognition | T-ASLP | SLP | ✅ | |||
Adaptation and customization for speech-to-text | T-ASLP | SLP | ✅ | |||
Text-based customization for speech-to-text | T-ASLP | SLP | ||||
Speech translation | T-ASLP | SLP | ||||
Machine translation of spoken/written language | T-ASLP | SLP | ✅ | |||
Natural language processing for speech translation | T-ASLP | SLP | ||||
Text-based customization for speech translation | T-ASLP | SLP | ||||
Applications and other topics in speech and language processing | T-ASLP | SLP |
Level - 1 | Level - 2 | Level - 3 | Journal | TC | ICASSP | ICIP | ||
---|---|---|---|---|---|---|
Applied Signal Processing Systems | T-SP | ASPS | ✅ | |||
Integrating signal processing and computing | T-SP | ASPS | ||||
Quantum and quantum-inspired signal processing | T-SP | ASPS | ✅ | |||
Neuromorphic computing | T-SP | ASPS | ✅ | |||
Edge and embedded computing | T-SP | ASPS | ✅ | |||
Energy-aware computing | T-SP | ASPS | ✅ | |||
Hardware accelerators | T-SP | ASPS | ✅ | |||
Resource-efficient machine learning | T-SP | ASPS | ✅ | |||
Signal processing and generative AI systems | T-SP | ASPS | ✅ | |||
Processing-in-memory signal processing systems | T-SP | ASPS | ✅ | |||
Signal processing application systems | T-SP | ASPS | ||||
Autonomous systems | T-SP | ASPS | ✅ | |||
Internet of things | T-SP | ASPS | ✅ | |||
Robotics | T-SP | ASPS | ✅ | |||
Radar, sonar and acoustic systems | T-SP | ASPS | ✅ | |||
Safe and trustworthy systems | T-SP | ASPS | ✅ | |||
Applications of generative AI and foundation models | T-SP | ASPS | ✅ | |||
Other emerging topics in signal processing systems | T-SP | ASPS | ✅ |
Level - 1 | Level - 2 | Level - 3 | Journal | TC | ICASSP | ICIP | ||
---|---|---|---|---|---|---|
Signal Processing Education | T-SP | SPEB | ✅ | |||
Curriculum development | T-SP | SPEB | ✅ | |||
Resouces and tools for signal processing education | T-SP | SPEB | ✅ | |||
Pedagogical models for signal processing education | T-SP | SPEB | ✅ | |||
Case studies in signal processing education | T-SP | SPEB | ✅ |