2022 IEEE International Conference on Acoustics, Speech and Signal Processing, 22-27 May 2022, Virtual Conference


ICASSP 2022 Paper Review Categories

* indicates that this line can be assigned as a paper's topic.
1:Applied Signal Processing Systems
 1.1*:[DIS-ARCH] Algorithm and architecture co-optimization
 1.2*:[DIS-PROG] Programmable Hardware (e.g. FPGA, SoC, ASIC and others) for DSP algorithms
 1.3*:[DIS-LPWR] Low-power signal processing techniques and architectures
 1.4*:[DIS-MLTC] Signal processing on multicore processors
 1.5*:[DIS-QUAN] Quantization
 1.6*:[DIS-SOCP] System-on-chip architectures for signal processing
 1.7*:[DIS-EMSA] Design and Implementation for emerging signal processing systems and applications
2:Audio and Acoustic Signal Processing
 2.1*:[AUD-MAAE] Modeling, Analysis and Synthesis of Acoustic Environments
  2.1.1:Acoustic system modeling
  2.1.2:Room response measurement
  2.1.3:Modeling and simulation
  2.1.4:Room geometry inference
  2.1.5:Reflector localization
  2.1.6:Reverberation time estimation
  2.1.7:Direct-to-reverberation ratio estimation
 2.2*:[AUD-CLAS] Detection and Classification of Acoustic Scenes and Events
  2.2.1:Acoustic scene classification and detection
  2.2.2:Acoustic event detection and classification
  2.2.3:Environmental audio analysis
 2.3*:[AUD-AMHI] Auditory Modeling and Hearing Instruments
  2.3.1:Human audition and psychoacoustics
  2.3.2:Binaural hearing
  2.3.3:Computational auditory scene analysis
  2.3.4:Perceptual and psychophysical models of audio algorithms and systems
  2.3.5:Hearing aids
  2.3.6:Cochlear implants
  2.3.7:Signal processing in hearing instruments
 2.4*:[AUD-ASAP] Acoustic Sensor Array Processing
  2.4.1:Far-field and near-field beamforming
  2.4.2:Acoustic sensor array processing
  2.4.3:Speech enhancement using acoustic sensor arrays
  2.4.4:Source localization and tracking
  2.4.5:Simultaneous localization and mapping of sources and sensors
  2.4.6:Time-delay estimation
  2.4.7:Array calibration
  2.4.8:Distributed and ad-hoc microphone arrays
  2.4.9:Deep learning methods for acoustic array processing
 2.5*:[AUD-NEFR] Active Noise Control, Echo Reduction and Feedback Reduction
  2.5.1:Active noise cancellation and suppression
  2.5.2:Single-channel and multichannel acoustic echo cancellation
  2.5.3:Echo path estimation and modeling
  2.5.4:Echo suppression
  2.5.5:Nonlinear echo reduction
  2.5.6:Double-talk detection
  2.5.7:Adaptive filter theory for audio applications
  2.5.8:Adaptive techniques for feedforward control
  2.5.9:Feedback cancellation
  2.5.10:Feedback suppression
  2.5.11:Transducer modeling for noise control and echo/feedback reduction
 2.6*:[AUD-SIRR] System Identification and Reverberation Reduction
  2.6.1:SIMO and MIMO identification
  2.6.2:Reverberation cancellation and suppression
  2.6.3:Blind deconvolution
  2.6.4:Channel shortening
  2.6.5:Channel equalization
 2.7*:[AUD-SEP] Audio and Speech Source Separation
  2.7.1:Single-channel and multichannel source separation
  2.7.2:Computational acoustic scene analysis
  2.7.3:NMF-based source separation
  2.7.4:Deep learning methods for source separation
 2.8*:[AUD-SEN] Signal Enhancement and Restoration
  2.8.1:Noise reduction
  2.8.2:Noise estimation, compensation, and equalization
  2.8.3:Deep learning methods for signal enhancement and restoration
  2.8.4:Audio de-noising and restoration
  2.8.5:Bandwidth expansion
  2.8.6:Clipping restoration,
  2.8.7:Near-end listening enhancement
 2.9*:[AUD-QIM] Quality and Intelligibility Measures
  2.9.1:Perceptual measures of audio quality
  2.9.2:Objective and subjective quality assessment
  2.9.3:Network audio quality assessment
  2.9.4:Speech intelligibility measures
 2.10*:[AUD-SARR] Spatial Audio Recording and Reproduction
  2.10.1:Analysis and synthesis of sound fields
  2.10.2:Wave-field synthesis
  2.10.3:Loudspeaker array processing
  2.10.6:Multipoint synthesis and binaural synthesis
  2.10.7:Crosstalk cancellation
  2.10.8:Virtual auditory environments
  2.10.9:Auralization, spatialization and virtualization
  2.10.10:Measurement and modeling of head-related transfer functions
  2.10.11:Binaural rendering
  2.10.12:Artificial reverberation algorithms
  2.10.13:Loudspeaker equalization and room compensation
 2.11*:[AUD-AMCT] Audio and Speech Modeling, Coding and Transmission
  2.11.1:Sparse representations
  2.11.2:Probabilistic modeling
  2.11.3:Low bit-rate and high-quality audio coding
  2.11.4:Scalable and lossless audio coding
  2.11.5:Spatial audio coding
  2.11.6:Joint source-channel coding
  2.11.7:Signal representations for coding
  2.11.8:Parametric and structured audio coding
  2.11.9:Psychoacoustic models for coding
  2.11.10:Low-delay audio coding
  2.11.11:Error detection, correction, and concealment
 2.12*:[AUD-MSP] Music Signal Analysis, Processing and Synthesis
  2.12.4:Models and representations for musical signals
  2.12.5:Pitch and multi-pitch estimation
  2.12.6:Audio feature extraction
  2.12.7:Melody, note, chord, key, and rhythm estimation and detection
  2.12.8:Automatic transcription
  2.12.9:Musical voice separation
  2.12.10:Instrument modeling
  2.12.11:Modeling of analog audio systems
  2.12.12:Audio effects
 2.13*:[AUD-MIR] Music Information Retrieval and Music Language Processing
  2.13.1:Content-based processing
  2.13.4:Structure analysis
  2.13.5:Content-based retrieval
  2.13.7:Data mining
  2.13.8:Symbolic music processing
  2.13.9:Grammar-based models
  2.13.10:Music composition and improvisation
  2.13.11:Score following and music accompaniment
  2.13.12:Music annotation and metadata
  2.13.13:Symbolic music corpora
 2.14*:[AUD-AUMM] Audio for Multimedia and Audio Processing Systems
  2.14.1:Joint processing of audio and video
  2.14.2:Human-machine audio interfaces
  2.14.3:Auditory displays
  2.14.4:Distant learning
  2.14.5:Augmented and virtual reality
  2.14.6:Hardware and software systems and implementations
  2.14.7:Consumer and professional audio
 2.15*:[AUD-BIO] Bioacoustics and Medical Acoustics
  2.15.1:Human body sounds analysis
  2.15.2:Investigation of sound production and reception in animals
 2.16*:[AUD-SEC] Audio security
  2.16.1:Audio security
  2.16.2:Audio privacy
  2.16.3:Audio analysis for forensics
  2.16.4:Audio watermarking and data hiding in audio streams
  2.16.5:Acoustic event detection for forensics
3:Biomedical Imaging and Signal Processing
 3.1:[CIS-MI] Medical Imaging: Image formation, reconstruction, restoration
  3.1.1*:[CIF-IRR] Image reconstruction and restoration
  3.1.2*:[CIS-TIM] Tomographic imaging
  3.1.3*:[CIS-MRI] Magnetic resonance imaging
  3.1.4*:[CIS-AIM] Acoustic Imaging: Computational acoustic and ultrasound imaging
 3.2:[BIO-MIA] Medical image analysis
  3.2.1*:Detection and estimation
  3.2.2*:Registration and motion analysis
  3.2.3*:Feature extraction and fusion
 3.3*:[BIO-BI] Biological imaging
 3.4*:[BIO-BIA] Biological image analysis
 3.5:[BIO] Biomedical signal processing
  3.5.1*:Detection and estimation
  3.5.2*:Feature extraction and fusion
  3.5.3*:[BIO-PHY] Physiological signal processing (ECG, EEG, EMG)
 3.6*:[BIO-BCI] Brain/human-computer interfaces
 3.7*:[BIO-INFR] Bioinformatics
4:Computational Imaging
 4.1:[IMT] Computational Imaging Methods and Models
  4.1.1*:[IMT-CIS] Coded Image Sensing
  4.1.2*:[IMT-CST] Compressed Sensing
  4.1.3*:[IMT-SIM] Statistical Image Models
  4.1.4*:[IMT-SLM] Sparse and Low Rank Models
  4.1.5*:[IMT-GIM] Graphical Image Models
  4.1.6*:[IMT-LBM] Learning-Based Models
  4.1.7*:[IMT-PIM] Perceptual Image Models
 4.2:[CIF] Computational Image Formation
  4.2.1*:[CIF-SBR] Sparsity-Based Reconstruction
  4.2.2*:[CIF-SBI] Statistically-Based Inversion
  4.2.3*:[CIF-MIF] Multi-Image & Sensor Fusion
  4.2.4*:[CIF-OBI] Optimization-based Inversion Methods
  4.2.5*:[CIF-MLI] Machine Learning based Computational Image Formation
 4.3:[CIS] Computational Imaging Systems
  4.3.1*:[CIS-CPH] Computational Photography
  4.3.2*:[CIS-MIS] Mobile Imaging
  4.3.3*:[CIS-PIS] Pervasive Imaging
  4.3.4*:[CIS-HCC] Human Centric Computing
  4.3.5*:[CIS-CMI] Microscopic Imaging
  4.3.6*:[CIS-SSI] Spectral Sensing
  4.3.7*:[CIS-TIM] Tomographic Imaging
  4.3.8*:[CIS-MRI] Magnetic resonance imaging
  4.3.9*:[CIS-AIM] Acoustic Imaging: Computational acoustic and ultrasound imaging
  4.3.10*:[CIS-RIM] Radar Imaging
  4.3.11*:[CIS-NCI] Novel Computational Imaging Systems
  4.3.12*:[CIS-NLC] Non-Linear Computational Imaging Systems
 4.4*:[HSS] Computational Imaging Hardware and Software
  4.4.1:[HSS-HPC] High-performance embedded computing systems
  4.4.2:[HSS-BDC] Big Data Computational Imaging: High performance computing
  4.4.3:[HSS-HDD] Integrated Hardware/Digital Design
  4.4.4:[HSS-NSS] Non-traditional Sensor Systems
5:Image, Video, and Multidimensional Signal Processing
 5.1:[IVSMR] Image & Video Sensing, Modeling, and Representation
  5.1.1*:[SMR-SEN] Image & Video Sensing and Acquisition
  5.1.2*:[SMR-REP] Image & Video Representation
  5.1.3*:[SMR-HPM] Perception and Quality Models for Images & Video
  5.1.4*:[SMR-SSM] Statistical and Structural Image/Video Models[SMR-SSM-SMD] Statistical-Model Based Methods[SMR-SSM-STM] Structural-Model Based Methods
 5.2:[IVTEC] Image & Video Processing Techniques
  5.2.1*:[TEC-LNV] Linear, Nonlinear and Variational Processing[TEC-LNV-FIL] Linear and Nonlinear Filtering of Images & Video[TEC-LNV-PDE] Partial Differential Equation Based Processing of Images & Video
  5.2.2*:[TEC-MRS] Multiresolution Processing of Images & Video
  5.2.3*:[TEC-INV] Inverse Problems in Image and Video Processing[TEC-INV-RST] Restoration and Enhancement[TEC-INV-ISR] Interpolation, Super-Resolution, and Mosaicing[TEC-INV-FOR] Formation and Reconstruction
  5.2.4*:[TEC-BIP] Biomedical and biological image processing
  5.2.5*:[TEC-MLI] Machine Learning for Image Processing
 5.3:[IVCOM] Image & Video Communications
  5.3.1*:[COM-CDG] Image and Video Coding[COM-CDG-LOC] Lossy Coding of Images & Video[COM-CDG-LLC] Lossless Coding of Images & Video[COM-CDG-ERC] Error Resilience and Channel Coding for Image & Video Systems
  5.3.2*:[COM-NET] Imaging & Video Networks
  5.3.3*:[COM-WSE] Image & Video Processing for Watermarking and Security
  5.3.4*:[COM-MMC] Multimedia Communications
 5.4:[IVELI] Electronic Imaging
  5.4.1*:[ELI-SCN] Scanned Image Analysis and Processing Image Scanning and Capture Scanned Document Analysis, Processing, and Coding
  5.4.2*:[ELI-COL] Color and Multispectral Imaging
  5.4.3*:[ELI-PRT] Printing and Halftoning
  5.4.4*:[ELI-STE] Stereoscopic and Multiview Processing, Display And Coding
  5.4.5*:[ELI-HDW] Hardware and Software Systems for Image & Video Processing
  5.4.6*:[ELI-PCP] Point Cloud Processing
  5.4.7*:[ELI-AVR] Image and Video Processing Augmented and Virtual Reality
 5.5:[IVARS] Image & Video Analysis, Synthesis, and Retrieval
  5.5.1*:[ARS-ANL] Image and Video Analysis[ARS-ANL-RBS] Region, Boundary, Texture and Shape Analysis[ARS-ANL-IVA] Image & Video Mid Level Analysis[ARS-ANL-BIM] Image & Video Biometric Analysis
  5.5.2*:[ARS-IIU] Image & Video Interpretation and Understanding
  5.5.3*:[ARS-SRE] Image & Video Storage and Retrieval
  5.5.4*:[ARS-SRV] Image & Video Synthesis, Rendering, and Visualization
6:Information Forensics and Security
 6.1*:[ADP] Anonymization And Data Privacy
 6.2*:[APC] Applied Cryptography
 6.3*:[BIO] Biometrics
 6.4*:[CIT] Communication And Information Theoretic Security
 6.5*:[CYB] Cybersecurity
 6.6*:[MMF] Multimedia Forensics
 6.7*:[HWS] Hardware Security
 6.8*:[MMH] Multimedia Content Hash
 6.9*:[NET] Network Security
 6.10*:[SUR] Surveillance
 6.11*:[USH] Usability And Human Factors
 6.12*:[WAT] Watermarking And Data Hiding
 6.13*:[MMH-OTHS] Forensics & Security Related Applications
7:Machine Learning for Signal Processing
 7.1*:[MLR-APPL] Applications of machine learning
 7.2*:[MLR-COGP] Cognitive information processing
 7.3*:[MLR-DEEP] Deep learning techniques
 7.4*:[MLR-DICT] Dictionary learning
 7.5*:[MLR-GKM] Graphical and kernel methods
 7.6*:[MLR-MFC] Matrix factorizations/completion
 7.7*:[MLR-ICA] Independent component analysis
 7.8*:[MLR-INFO] Information-theoretic learning
 7.9*:[MLR-LEAR] Learning theory and algorithms
 7.10*:[MLR-LMM] Learning from multimodal data
 7.11*:[MLR-MUSAP] Applications in music and audio processing
 7.12*:[MLR-PRCL] Pattern recognition and classification
 7.13*:[MLR-PERF] Bounds on performance
 7.14*:[MLR-SBML] Subspace and manifold learning
 7.15*:[MLR-SLER] Sequential learning; sequential decision methods
 7.16*:[MLR-SSEP] Source separation
 7.17*:[MLR-TNSR] Tensor-based signal processing
 7.18*:[SMDSP-SAP] Sparsity-aware processing
 7.19*:[OTH-BGDT] Big Data
 7.20*:[MLR-DFED] Distributed/Federated learning
 7.21*:[MLR-REI] Reinforcement learning
 7.22*:[MLR-TRL] Transfer learning
 7.23*:[MLR-SSUP] Self-supervised and semi-supervised learning
 7.24*:[MLR-MLON] Machine learning over wireless networks
8:Multimedia Signal Processing
 8.1:Signal Processing for Multimedia Applications
  8.1.1*:[ASLAS] Audio/Speech/Language Analysis and Synthesis
  8.1.2*:[CCMT] Compression, Coding, Media Conversion and Transcoding
  8.1.3*:[IVGAS] Image/Video/Graphics Analysis and Synthesis
  8.1.4*:[SYNA] Integration of Synthetic and Natural Audio/Video
  8.1.5*:[3DA] 3-D Audio Signal Processing
  8.1.6*:[3DV] 3-D Video Signal Processing
 8.2*:Technology Components and System Integration
 8.3:Human Centric Multimedia
  8.3.1*:[MHMI] Multimodal Human-machine Interfaces and Interaction
  8.3.2*:[MPIM] Multimodal Perception, Integration, and Multisensory Fusion
  8.3.3*:[QAUE] Subjective and Objective Quality Assessment, and User Experience
 8.4:Multimedia Environments
  8.4.1*:[AVEW] Audio-visual-haptic Environments and Workspaces
  8.4.2*:[MTAC] Multimodal Telepresence and Collaboration
  8.4.3*:[VAAR] Virtual and Augmented Reality
 8.5:Multimedia Databases and File Systems
  8.5.1*:[BIGM] Big Data Support for Multimedia
  8.5.2*:[KNOW] Knowledge and Semantics Modeling for Multimedia Databases
  8.5.3*:[SEAR] Multimedia Search and Retrieval
 8.6*:Multimedia Applications
 8.7*:Standards and Related Issues in Multimedia
 8.8:Multimedia Communications and Networking
  8.8.1*:[MCCC] Media Cloud Computing and Communication
  8.8.2*:[MSTRC] Multimedia Streaming, Transport, Error Resilience and Concealment
  8.8.3*:[SCNC] Distributed/Cooperative Networks and Communication
  8.8.4*:[WMMM] Wireless and Mobile Multimedia
 8.9:Emerging Areas in Multimedia
  8.9.1*:[DLMA] Deep Learning for Multimedia Analysis
  8.9.2*:[DLMP] Deep Learning for Multimedia Processing
9:Sensor Array and Multichannel Signal Processing
 9.1*:[SAM-APPL] Applications of sensor & array multichannel processing
 9.2*:[SAM-BEAM] Beamforming
 9.3*:[SAM-CALB] Array calibration
 9.4*:[SAM-CSSM] Compressed sensing and sparse modeling
 9.5*:[SAM-DOAE] Direction of arrival estimation and source localization
 9.6*:[SAM-GSSP] Geophysical and seismic signal processing
 9.7*:[SAM-IMGA] Inverse methods and imaging with array data
 9.8*:[SAM-LRNM] Learning models and methods for multi-sensor systems
 9.9*:[SAM-MCHI] Multichannel processing, identification, and modelling
 9.10*:[SAM-MAPR] Microphone array processing
 9.11*:[SAM-NWAV] Non-wave based array processing
 9.12*:[SAM-PERF] Performance analysis and bounds
 9.13*:[SAM-SDET] Source detection and separation
 9.14*:[SAM-SENS] Multi-sensor remote sensing of the environment
 9.15*:[SAM-STAP] Space-time adaptive methods
 9.16*:[SAM-TNSR] Tensor-based signal processing for multi-sensor systems
 9.17*:[SAM-MUCN] Multi-user and cooperative networks
 9.18*:[SAM-CAMS] Computational advances for multi-sensor systems
 9.19*:[RAS-DTCL] Target detection, classification, localization
 9.20*:[RAS-LCLZ] Source localization
 9.21*:[RAS-MIMO] MIMO Radar and waveform design
 9.22*:[RAS-SARI] Synthetic aperture radar/sonar and imaging
 9.23*:[RAS-SONR] Sonar and underwater signal processing
 9.24*:[RAS-TRCK] Target tracking
 9.25*:[BIO-SENS] Sensor arrays for medical signal and image processing
 9.26*:[SPC-MIMO] Multiple-input multiple-output communication systems
 9.27*:[SPC-MMIMO] Massive MIMO communication systems
 9.28*:[SSP-PARE] Parameter Estimation
10:Signal Processing for Communications and Networking
 10.1*:[SPC-MOD] Modulation, demodulation, encoding and decoding
  10.1.1:[SPC-MEPB] Modulation, encoding, pre-coding and beamforming
  10.1.2:[SPC-DETC] Detection, estimation, and demodulation
  10.1.3:[SPC-CMPR] Signal representation, coding and compression
 10.2*:[SPC-CHAN] Channel modelling and estimation
  10.2.1:[SPC-SYNC] Acquisition, synchronization and tracking
  10.2.2:[SPC-CHAN] Channel characterization, modelling, estimation and equalization
 10.3*:[SPC-MIMO] Multiple-Input Multiple-Output
  10.3.1:[SPC-MIMO] Multiple-input multiple-output communication systems
  10.3.2:[SPC-MMIMO] Massive MIMO communication systems
  10.3.3:[SPC-DMIMO] Distributed MIMO
 10.4*:[SPC-HIGH] High frequency and wideband communication
  10.4.1:[SPC-MMTH] Millimetre-wave and terahertz communications
  10.4.2:[SPC-UWBC] Ultra wideband communications
 10.5*:[CNS-LLC] Low latency communications
 10.6*:[SPC-INTF] Interference management techniques
 10.7*:[SPC-MULT] Multi-carrier and spread spectrum techniques
  10.7.1:[SPC-MULT] Multi-carrier, OFDM, and DMT communications
  10.7.2:[SPC-CDSS] CDMA and spread spectrum communications
  10.7.3:[SPC-DSLP] Digital subscriber loops and powerline communication
 10.8*:[SPCN-NETW] Networks and Network Resource allocation
  10.8.1:[SPC-RSRC] Scheduling and resource management
  10.8.2:[SPC-CROP] Cross-layer optimization
  10.8.3:[CNS-NSPRA] Optimal network signal processing and resource allocation
  10.8.4:[CNS-CLRD] Cross-Layer design
  10.8.5:[CNS-RSMG] Resource management issues
  10.8.6:[CNS-SQP] Scheduling and queuing protocols
  10.8.7:[MLR-MLON] Machine learning over wireless networks
 10.9*:[CNS-SENS] Sensor and ad-hoc networks
  10.9.1:[CNS-APPL] Applications of sensor networks
  10.9.2:[CNS-ADHC] Ad-hoc wireless networks
 10.10*:[SPC-COMP] Compensation and calibration of front end components
 10.11*:[SPCN-ENGY] Energy efficiency in communications
  10.11.1:[SPC-EAC] Energy Aware Communications
  10.11.2:[CNS-ENGY] Energy efficient sensor network algorithms
 10.12*:[SPCN-DIST] Distributed, adaptive, and collaborative communication techniques
  10.12.1:[SPC-SPCR] Signal processing for cognitive radios
  10.12.2:[CNS-SPDCN] Signal processing for distributed communications and networking
  10.12.3:[CNS-SPCN] Signal processing for cooperative networking
  10.12.4:[CNS-CCMCT] Cooperative and coordinated multi-cell techniques
  10.12.5:[CNS-DSCD] Distributed source and channel decoding
 10.13*:[SPC-ML] Machine Learning for Communications
 10.14*:[SPC-PERF] Information theory and performance bounds
  10.14.1:[SPC-INFO] Information-theoretic studies
  10.14.2:[SPC-PERF] Performance analysis and bounds
 10.15*:[SPC-SPARCO] Sparse SP techniques for communication
 10.16*:[SPC-APP] Applications involving of signal processing for communications
 10.17*:[CIT-PHYS] Physical Layer Security
  10.17.1:[SPC-PHYS] Physical layer security
  10.17.2:[CIT-COM-JAM] Jamming and anti-jamming techniques
  10.17.3:[CIT-COM-COV] Covert or stealthy communication via physical layers
  10.17.4:[CIT-COM-COOP] Security and trust in cooperative communications
  10.17.5:[CIT-COM-MIMO] Security and trust in MIMO and multiple-access techniques
  10.17.6:[CIT-COM-COG] Security in cognitive radio
  10.17.7:[CIT-PHY] Physical layer security
  10.17.8:[CIT-PHY-SKEY] Secret key extraction from channels
  10.17.9:[CIT-PHY-COD] Coding for physical layer security
  10.17.10:[CIT-PHY-MIMO] Physical layer security in MIMO systems
  10.17.11:[CIT-INF] Information theoretic security
  10.17.12:[CIT-INF-SECC] Security over channels
 10.18*:[SPC-OPTWC] Optical wireless communications
 10.19*:[SPC-NTC] Signal processing for non-terrestrial communications
 10.20*:[MLR-DFED] Distributed/Federated learning
11:Signal Processing Theory and Methods
 11.1:[SMDSP] Sampling, Multirate Signal Processing and Digital Signal Processing
  11.1.1*:[SMDSP-SAM] Sampling Theory, Analysis and Methods[SMDSP-ALGO] Algorithm analysis[SMDSP-SAMP] Sampling, extrapolation, and interpolation[SMDSP-QUAN] Quantization effects
  11.1.2*:[SMDSP-CNS] Compressed and Nonuniform Sampling[SMDSP-CPSL] Compressive and nonuniform sampling[SMDSP-ASAL] Adaptive Sensing Algorithms
  11.1.3*:[SMDSP-RECO] Algorithms for signal filtering, restoration, enhancement, and reconstruction
  11.1.4*:[SMDSP-MRA] Multiresolution Analysis, filter banks, and wavelets[SMDSP-APPL] Applications of digital and multirate signal processing[SMDSP-BANK] Filter bank design and theory[SMDSP-MULT] Multirate processing and multiresolution methods[SMDSP-TFSR] Time-frequency analysis and signal representation
  11.1.5*:[SMDSP-FAT] Fast Algorithms and Transforms[SMDSP-FAST] Fast algorithms for digital signal processing[SMDSP-TRSF] Transforms for signal processing
  11.1.6*:[SMDSP-SAP] Sparsity-aware processing
 11.2:[SIPG] Signal and Information Processing over Graphs
  11.2.1*:[SIPG-SA] Statistical Approaches (models, etc.)[NEG-INFO] Information-theoretic studies[SPIG-STOC] Stochastic processes over graphs (T-SIPN & TSP)[SIPG-MEND] Modeling and estimation of network dynamics (T-SIPN)[SIPG-MNE] Modeling of network evolution (T-SIPN)
  11.2.2*:[SIPG-DA] Deterministic Approaches (graph filtering, graph transforms, etc.)[NEG-SPGR] Signal processing over graphs (filtering, transforms, etc)[NEG-SAMP] Sampling over graphs[SIPG-DIS] Distributed processing of graph data (T-SIPN)
  11.2.3*:[SIPG-GRA] Graph Representations and Analysis[NEG-GRAN] Graph analysis for signal processing[NEG-SPGT] Spectral graph theory and algebraic topology algorithms[NEG-SYLO] System level optimization
  11.2.4*:[SIPG-AL] Adaptation and Learning Over Graphs[NEG-ADLE] Adaptation and learning over graphs[NEG-ASAL] Adaptive sensing algorithms
 11.3:[OPT] Optimization Methods for Signal Processing
  11.3.1*:[OPT-CVXR] Convex optimization and relaxation for SP
  11.3.2*:[OPT-DOPT] Distributed optimization for SP
  11.3.3*:[OPT-NCVX] Non-convex methods for SP
  11.3.4*:[OPT-SPARSE] Sparse optimization techniques for SP
 11.4*:[OTH-QUAN] Quantum signal processing
 11.5*:[ASP] Adaptive Signal Processing
  11.5.1:[ASP-AFAD] Adaptive filter analysis and design
  11.5.2:[ASP-APPL] Applications of adaptive filters
  11.5.3:[ASP-FAST] Fast algorithms for adaptive filtering
  11.5.4:[ASP-FREQ] Frequency-domain and subband adaptive filtering
 11.6:[SSP] Statistical Signal Processing
  11.6.1*:[SSP-DTM] Detection Theory and Methods[SSP-DETC] Detection[SSP-RDET] Robust detection, estimation, and tracking
  11.6.2*:[SSP-ETM] Estimation Theory and Methods[SSP-PARE] Parameter estimation[SSP-IDEN] System identification[SSP-SPEC] Spectral analysis and spectral estimation
  11.6.3*:[SSP-CLAS] Classification methods
  11.6.4*:[SSP-ANA] Analysis[SSP-PERF] Performance analysis and bounds[SSP-SSAN] Statistical signal analysis
  11.6.5*:[SSP-LNSP] Linear and Nonlinear Systems and Signal Processing[SSP-DECO] Deconvolution[SSP-FILT] Filtering[SSP-SSEP] Signal separation[SSP-REST] Signal restoration[SSP-NSSP] Nonstationary statistical signal processing[SSP-NGAU] Non-Gaussian signals and noise
  11.6.6*:[SSP-BSP] Bayesian signal processing
  11.6.7*:[SSP-MM] Models and Methods[SSP-HIER] Hierarchical models & tree structured signal processing[SSP-HOSM] Higher-order statistical methods[SSP-NPAR] Non-parametric methods[SSP-SNMD] Signal and noise modeling[SSP-SYSM] System modeling[SSP-SPRS] SP methods for structured low dimensional models
  11.6.8*:[SSP-TRAC] Tracking algorithms
  11.6.9*:[SSP-APPL] Applications of statistical signal processing techniques[OTH-GSSP] Green and sustainable signal processing[OTH-NDTE] Non-destructive testing and evaluation
 11.7:Signal Processing over Networks
  11.7.1*:[DPON] Distributed Processing and Optimization over Networks[NEG-ADHC] Ad-hoc wireless networks[NEG-CLRD] Cross-layer design[ADEL-DAN] Distributed adaptation over networks[NEG-RSMG] Resource management issues[NEG-ENGY] Energy efficient sensor network algorithms[NEG-FUSE] Data fusion from multiple sensors[ADEL-CNS] Consensus over network systems[ADEL-ONS] Optimization over network systems[MLR-DFED] Distributed/Federated learning
  11.7.2*:[EDLN] Estimation, Detection and Learning over Networks[NEG-LOCL] Source localization in sensor networks[NEG-LRNM] Learning models and methods[ADEL-DDE] Distributed detection and estimation[ADEL-DLN] Distributed learning over networks[ADEL-SLN] Sequential learning over networks[ADEL-DMN] Decision making over networks[MLR-SSUP] Self-supervised and semi-supervised learning[MLR-REI] Reinforcement learning
  11.7.3*:[NEG-APPL] Applications of sensor networks
 11.8:[SMDSP-SAP] Sparsity-aware Processing
  11.8.1*:Sparse/low-dimensional signal recovery, parameter estimation and regression
  11.8.2*:Structured matrix factorization, low-rank models, matrix completion
  11.8.3*:Dictionary learning; subspace and manifold learning
13:Speech and Language Processing
 13.1*:[SPE-SPRD] Speech Production
 13.2*:[SPE-SPER] Speech Perception and Psychoacoustics
 13.3:[SPE-ANLS] Speech Analysis
  13.3.1*:Signal, intonation, and paralinguistics modeling
  13.3.3*:Language Disorders
 13.4:[SPE-SYNT] Speech Synthesis and Generation
  13.4.1*:Voice Conversion
  13.4.2*:Machine learning for speech synthesis, including end-to-end methods
  13.4.3*:General Topics in Speech Synthesis
 13.5*:[SPE-CODI] Speech Coding
 13.6:[SPE-ENHA] Speech Enhancement and Separation
  13.6.1*:Speech Enhancement Methods, including Deep Learning Methods
  13.6.2*:Speech Separation and Denoising
  13.6.3*:Multichannel Methods for Speech Enhancement/Separation (e.g., Beamforming)
 13.7*:[SPE-RECO] Acoustic Modeling for Automatic Speech Recognition
 13.8*:[SPE-ROBU] Robust Speech Recognition
 13.9*:[SPE-ADAP] Speech Adaptation/Normalization
 13.10:[SPE-LVCR] Large Vocabulary Continuous Recognition/Search
  13.10.1*:End-to-end approaches
  13.10.2*:Other LVSCR approaches
 13.11*:[SPE-MULT] Multilingual Recognition and Identification
 13.12:[SPE-GASR] General Topics in Speech Recognition
  13.12.1*:Hardware/network-aware methods
  13.12.2*:Word spotting
  13.12.3*:Metadata (e.g., emotion, speaker, accent) extraction from acoustics
  13.12.4*:New algorithms and approaches
  13.12.5*:Corpora, annotation, and other resources
  13.12.6*:Lexical Modeling and Access
  13.12.7*:Resource Constrained Speech Recognition
  13.12.8*:Other topics in speech recognition
 13.13:[SPE-SPKR] Speaker Recognition and Characterization
  13.13.1*:Speaker verification and anti-spoofing
  13.13.2*:Speaker recognition/identification
  13.13.3*:Speaker diarization
 13.14*:[SPE-VAD] Voice Activity Detection and End-pointing
 13.15*:[HLT-LANG] Language Modeling
 13.16*:[HLT-MTSW] Machine Translation for Spoken and Written Language
 13.17*:[HLT-UNDE] Spoken Language Understanding and Computational Semantics
 13.18*:[HLT-DIAL] Discourse and Dialog
 13.19*:[HLT-SDTM] Spoken Document Retrieval and Text Mining
 13.20*:[HLT-STPA] Segmentation, Tagging, and Parsing
 13.21*:[HLT-LACL] Language Acquisition and Learning
 13.22*:[HLT-MLMD] Machine Learning Methods for Language
 13.23*:[HLT-LRES] Language Resources and Systems
 13.24*:[HLT-MMPL] Multimodal Processing of Language
14:[OTH-EDU] Signal Processing Education
 14.1*:Resources and tools for teaching signal processing
 14.2*:Novel pedagogical approaches in signal processing (graduate, undergraduate, K-12, continuing education)
 14.3*:Case study reports in signal processing education