ICASSP 2019 Paper Review Categories

* indicates that this line can be assigned as a paper's topic.

1:Audio and Acoustic Signal Processing
 1.1:Modeling and Analysis of Acoustic Environments
  1.1.1*:Acoustic system modeling
  1.1.2*:Acoustic system and room response measurement, modeling and simulation
  1.1.3*:Room geometry inference and reflector localization
  1.1.4*:Reverberation time and direct-to-reverberant ratio estimation
 1.2:Detection and Classification of Acoustic Scenes and Events
  1.2.1*:Acoustic scene classification and detection
  1.2.2*:Acoustic event detection and classification
  1.2.3*:Environmental audio analysis
  1.2.4*:Bioacoustics and medical acoustics
 1.3:Auditory Modeling and Hearing Instruments
  1.3.1*:Human audition and psychoacoustics, binaural hearing
  1.3.2*:Signal processing in hearing aids and cochlear implants
  1.3.3*:Computational auditory scene analysis
  1.3.4*:Perceptual and psychophysical models of audio algorithms and systems
 1.4:Acoustic Sensor Array Processing
  1.4.1*:Source localization and tracking, SLAM, and array calibration
  1.4.2*:Microphone array design and beamforming methods
  1.4.3*:Multi-microphone speech enhancement
  1.4.4*:Distributed and ad-hoc microphone arrays (acoustic sensor networks)
  1.4.5*:Deep-learning based multi-microphone acoustic signal processing
 1.5:Active Noise Control, Echo Reduction and Feedback Reduction
  1.5.1*:Active noise cancellation and suppression
  1.5.2*:Acoustic echo cancellation, double-talk detection
  1.5.3*:Acoustic feedback cancellation and suppression
 1.6:System Identification and Reverberation Reduction
  1.6.1*:SIMO and MIMO acoustic system identification
  1.6.2*:Single-microphone dereverberation
  1.6.3*:Multi-microphone dereverberation and channel equalization
  1.6.4*:Deep-learning based dereverberation (inc. generative or adversarial methods)
 1.7:Audio and Speech Source Separation
  1.7.1*:Single-microphone separation
  1.7.2*:Multi-microphone separation
  1.7.3*:NMF-based separation
  1.7.4*:Deep-learning-based separation (inc. generative or adversarial methods)
 1.8:Signal Enhancement and Restoration
  1.8.1*:Single microphone noise estimation and speech enhancement
  1.8.2*:Deep-learning based signal enhancement and restoration (inc. generative or adversarial methods)
  1.8.3*:Bandwidth expansion
  1.8.4*:Audio denoising, equalization and clipping restoration
 1.9*:Quality and Intelligibility Measures
 1.10:Spatial Audio Recording and Reproduction
  1.10.1*:Analysis and synthesis of sound fields
  1.10.2*:Spatial sound reproduction (inc. wavefield synthesis, ambisonics, multipoint synthesis, binaural synthesis)
  1.10.3*:Loudspeaker array processing, crosstalk cancellation, loudspeaker equalization and room compensation
  1.10.4*:Measurement and modeling of head-related transfer functions
  1.10.5*:Artificial reverberation algorithms
 1.11:Audio for Multimedia and Audio Processing Systems
  1.11.1*:Audio and speech modeling, coding and transmission
  1.11.2*:Joint audio-video processing
  1.11.3*:Virtual/augmented reality
  1.11.4*:User interfaces for audio editing/processing/analysis/synthesis
  1.11.5*:Deep-learning based (inc. generative or adversarial) methods for audio coding/processing and multimedia
 1.12:Music Signal Analysis, Processing and Synthesis
  1.12.1*:Models and representation for music signals
  1.12.2*:Pitch and multi-pitch estimation
  1.12.3*:Source separation of music and vocal signals
  1.12.4*:Melody, note, chord, key, and rhythm estimation and detection
  1.12.5*:Modeling of analog audio systems and audio effects
  1.12.6*:Deep-learning based music signal analysis, processing and synthesis
 1.13*:Music Information Retrieval and Music Language Processing
  1.13.1:Transcription, annotation and structure analysis
  1.13.2*:Content-based music analysis, classification and processing
  1.13.3*:Symbolic music processing, symbolic music corpora and grammar-based models
  1.13.4*:Music composition, improvisation and score following
  1.13.5*:Deep-learning based music information retrieval
  1.13.6*:Fingerprinting and data mining
 1.14:Audio security
  1.14.1*:Audio security and audio privacy
  1.14.2*:Audio analysis for forensics
  1.14.3*:Audio watermarking and data hiding in audio streams
  1.14.4*:Acoustic event detection for forensics
2:Bio Imaging and Signal Processing
 2.1*:Medical imaging
  2.1.1*:Image formation
  2.1.2*:Reconstruction and restoration
  2.1.3*:Computed tomography (CT, PET or SPECT)
  2.1.4*:Biomedical Imaging
  2.1.5*:Magnetic resonance imaging
  2.1.6*:Ultrasound imaging
 2.2:Medical image analysis
  2.2.3*:Feature extraction and classification
 2.3:Bioimaging and microscopy
  2.3.1*:Cellular and molecular imaging
  2.3.2*:Deconvolution and inverse problems
  2.3.3*:Segmentation and analysis
  2.3.4*:Tracking and motion analysis
 2.4:Biomedical signal processing
  2.4.1*:Physiological signals (ECG, EEG, ...)
  2.4.2*:Detection and estimation
  2.4.3*:Feature extraction and classification
  2.4.4*:Multi-channel processing
  2.5.1*:Genomics and proteomics
  2.5.2*:Computational biology and biological networks
3:Design and Implementation of Signal Processing Systems
 3.1*:Algorithm and architecture co-optimization
 3.2*:Compilers and tools for DSP implementation
 3.3*:DSP algorithm implementation in hardware and software
 3.4*:Low-power signal processing techniques and architectures
 3.5*:Programmable and reconfigurable DSP architectures
 3.6*:System-on-chip architectures for signal processing
 3.7*:Hardware security
 3.8:Design and Implementation of emerging signal processing systems
  3.8.1*:Machine learning
  3.8.2*:Neuromorphic computing
  3.8.3*:Internet of things
  3.8.4*:Big data
  3.8.5*:Signal Processing for storage
 3.9:Signal processing for emerging applications
  3.9.1*:Autonomous vehicles
  3.9.2*:Financial technology (FinTech)
  3.9.3*:Virtual Reality/Augmented Reality
4:Image, Video, and Multidimensional Signal Processing
 4.1:Image/Video Coding
  4.1.1*:Still Image Coding
  4.1.2*:Video Coding
  4.1.3*:Stereoscopic and 3-D Coding
  4.1.4*:Distributed Source Coding
  4.1.5*:Image/Video Transmission
 4.2:Image/Video Processing
  4.2.1*:Image Filtering
  4.2.4*:Image Segmentation
  4.2.5*:Video Segmentation and Tracking
  4.2.6*:Morphological Processing
  4.2.7*:Stereoscopic and 3-D Processing
  4.2.8*:Image Feature Extraction
  4.2.9*:Image Analysis
  4.2.10*:Video Feature Extraction
  4.2.11*:Video Analysis
  4.2.14*:Interpolation and Super-resolution
  4.2.15*:Motion Detection and Estimation
 4.3:Image Formation
  4.3.1*:Remote Sensing Imaging
  4.3.2*:Geophysical and Seismic Imaging
  4.3.3*:Optical Imaging
  4.3.4*:Synthetic-Natural Hybrid Image Systems
 4.4:Image Scanning, Display, and Printing
  4.4.1*:Scanning and Sampling
  4.4.2*:Quantization and Halftoning
  4.4.3*:Color Reproduction
  4.4.4*:Image Representation and Rendering
  4.4.5*:Display and Printing Systems
  4.4.6*:Image Quality Assessment
 4.5:Image/Video Storage, Retrieval
  4.5.1*:Image and Video Databases
  4.5.2*:Image Indexing and Retrieval
  4.5.3*:Video Indexing, Retrieval and Editing
5:Information Forensics and Security
 5.1:Watermarking and Steganography
  5.1.1*:Content transforms
  5.1.2*:Theoretical watermarking models
  5.1.3*:Watermark embedding/detection algorithms
  5.1.4*:Perceptual modeling and watermark shaping
  5.1.5*:Watermark resynchronization techniques
  5.1.6*:Watermark benchmarking and security analysis
 5.2:Passive Forensic Analysis
  5.2.1*:Sensor and channel forensics
  5.2.2*:Signal processing forensics
  5.2.3*:Behavior and social interactions forensics
  5.2.4*:Communications forensics
  5.2.5*:Anti-forensics and countermeasures
  5.2.6*:Fusion and cross-references
  5.2.8*:Forensic protocols
  5.3.1*:Biometric sensor design
  5.3.2*:Feature extraction algorithms
  5.3.3*:Feature matching and fusion techniques
  5.3.4*:Biometric security and privacy
  5.3.5*:Databases and performance evaluation
 5.4:Multimedia Content Hash
  5.4.1*:Global content hash functions
  5.4.2*:Local features detectors
  5.4.3*:Local features descriptors
  5.4.4*:Nearest neighbor search algorithms
  5.4.5*:Benchmarking and reference datasets
  5.4.6*:Security analysis
 5.5:Communications and Physical Layer Security
  5.5.1*:Jamming and anti-jamming
  5.5.2*:Covert or stealthy communication
  5.5.3*:Secret key extraction from channels
  5.5.4*:Security and trust in communications
  5.5.5*:Threats and attacks analysis
 5.6:Information Theoretic Security
  5.6.1*:Security over channels: wire-tap, broadcast, multiple access, etc
  5.6.2*:Data security and cryptography
  5.6.3*:Secret key generation, sharing, distribution
  5.6.4*:Privacy and trust
  5.6.5*:Shannon theory
 5.7:Signal Processing and Cryptography
  5.7.1*:Multimedia encryption
  5.7.2*:Signal processing in the encrypted domain
  5.7.3*:Traitor tracing codes
  5.7.4*:Visual secret sharing
  5.7.5*:Side channel attacks
 5.8*:Video- and Signal-based Surveillance
  5.8.1:Sensor-centric processing
  5.8.2*:Processing, detection, tracking & recognition
  5.8.3*:Visualization and interaction concepts for surveillance systems
  5.8.4*:Analytics, situation awareness and decision making
  5.8.5*:Security and privacy
 5.9:Security-related applications
  5.9.1*:Surveillance and intelligence gathering
  5.9.2*:Access control systems
  5.9.3*:Content protection, identification and monitoring
  5.9.4*:Content authentication and tamper detection
  5.9.5*:Cloud and distributed computing systems
  5.9.6*:Smart grid and power/energy systems
  5.9.7*:Social media and network systems
  5.9.8*:Privacy enhancing technologies
  5.9.9*:Crime scene analysis
  5.9.10*:Signal processing for secure documents
6:Industry DSP Technology
 6.1:Emerging Signal Processing Applications
  6.1.1*:Industrial IoT Applications
  6.1.2*:Display, Cameras and 3D Technology
  6.1.3*:Applications of Social Signal Processing
  6.1.4*:Green Technologies for Signal Processing
  6.1.5*:DSP for Smart Devices
  6.1.6*:Cloud/Fog/Edge Computing and Services
  6.1.7*:Signal Processing for Mobile and Embedded Application
  6.1.8*:Business Intelligence and Big Data Applications
  6.1.9*:Digital and Software RF Processing
  6.1.10*:Terahertz Technology and Signal Processing
  6.1.11*:Blockchain Applications
 6.2:DSP Tools and Rapid Prototyping
  6.2.1*:DSP Simulation Tools
  6.2.2*:Rapid Prototyping and Languages
  6.2.3*:DSP Libraries
  6.2.4*:Operating Systems
  6.2.5*:Programming Models and Languages
 6.3:Communication Technologies
  6.3.1*:Cellular and Satellite Telephony
  6.3.2*:Data Communications and Networking
  6.3.3*:Software-Defined Radios
  6.3.5*:Smart Home and Smart City
 6.4:Speech Processing Applications
  6.4.1*:Speaker Recognition
  6.4.2*:Speech Compression
  6.4.3*:Speech Enhancement
  6.4.4*:Speech Recognition
  6.4.5*:Speech Synthesis
 6.5:Multimedia and DTV Technologies
  6.5.1*:DSP Implementations of Music, Speech, and Audio
  6.5.2*:Image and Video Applications
  6.5.3*:Standards and Format Conversions
  6.5.4*:Internet and Teleconferencing
 6.6*:Adaptive Interference Cancellation
  6.6.1*:Smart Antennas
  6.6.2*:Active Sound Reduction
  6.6.3*:Acoustic and Electrical Noise and Echo Cancellation
  6.6.4*:Hands-Free Telephony
  6.6.5*:Smart and AI Speakers
  6.6.6*:SP for Wearable Devices
 6.7:Automotive Applications
  6.7.1*:Intelligent Dashboards, Vehicles, and Highways (IVHS)
  6.7.2*:Engine Management
  6.7.3*:Route Planning and Tracking
  6.7.4*:New Consumer Applications
  6.7.5*:Power Systems and Motor Controls
 6.8:Defense and Security Applications
  6.8.1*:Optical Correlation
  6.8.2*:Decluttering Target Identification and Tracking
  6.8.3*:DSP-Based Cryptography, Stenography, and Watermarking
  6.8.4*:Radar and Sonar
 6.9:DSP Chips and Architectures
  6.9.1*:Mixed Signal Processing
  6.9.2*:Special-Purpose and FPGA DSPs
  6.9.3*:Host-Based Signal Processing
  6.9.4*:Multiprocessor Architectures
  6.9.5*:Converge of Algorithm, Computing and Memory Access
  6.9.6*:SP for Data Memory and Storage Applications
 6.10:Other ITT Topics
  6.10.3*:Wearable Technology
  6.10.4*:SP Related Standard Proposals
  6.10.5*:Quantum Computing and Communication
  6.10.6*:Other Sub-Topics (Industry and Emerging Applications)
7:Machine Learning for Signal Processing
 7.1*:Learning Theory and Modeling
 7.2*:Bayesian Learning and Modeling
 7.3*:Sequential learning; sequential decision methods
 7.4*:Information-theoretic learning
 7.5*:Neural networks and deep learning
 7.6*:Graphical and kernel models
 7.7*:Bounds on performance
 7.8*:Source separation and independent component analysis
 7.9*:Signal detection, Pattern Recognition and Classification
 7.10*:Bioinformatics Applications
 7.11*:Biomedical Applications and Neural Engineering
 7.12*:Intelligent Multimedia and Web Processing
 7.13*:Communications Applications
 7.14*:Speech and Audio Processing Applications
 7.15*:Image and Video Processing Applications
 7.16*:Tensor and Structured Matrix Methods
 7.17*:Machine learning for big data
 7.18*:Large scale learning
 7.19*:Dictionary learning, subspace and manifold learning
 7.20*:Semi-supervised and unsupervised learning
 7.21*:Active and reinforcement learning
 7.22*:Learning from multimodal data
 7.23*:Resource efficient machine learning
 7.24*:Other Applications
8:Multimedia Signal Processing
 8.1:Multimodal signal processing
  8.1.1*:Joint processing/presentation of audio-visual and multimodal information
  8.1.2*:Fusion/fission of sensor information or multimodal data
  8.1.3*:Integration of media, art, and multimedia technology
  8.1.4*:Analysis and feature extraction of multimodal data
 8.2:Virtual reality and 3D imaging
  8.2.1*:2D and 3D graphics/geometry coding and animation
  8.2.2*:3D audio and video processing
  8.2.3*:Point cloud processing
  8.2.4*:Virtual reality and mixed-reality in networked environments
 8.3:Big data and learning-based media processing
  8.3.1*:Big data and cloud media processing
  8.3.2*:Traditional learning-based media processing
  8.3.3*:Deep learning-based media processing
  8.3.4*:Multimedia data mining
 8.4:Graph signal processing for multimedia
  8.4.1*:Graph-based audio, image and video processing
  8.4.2*:Prediction and learning in graphs for multimedia
  8.4.3*:Applications for graph signal processing
 8.5:Multimedia communications and networking
  8.5.1*:Wireless and mobile multimedia communication
  8.5.2*:Media streaming, media content distribution, and storage
  8.5.3*:Quality of service provisioning
  8.5.4*:Cross-layer design for multimedia communication
  8.5.5*:Overlay, peer-to-peer, and peer-assisted networking for multimedia
  8.5.6*:Home networking for multimedia
  8.5.7*:Location-aware multimedia computing
  8.5.8*:Multimedia sensor and ad hoc networks
  8.5.9*:Media compression and related standardization activities
  8.5.10*:Distributed source and source-channel coding
  8.5.11*:Social network and media sharing
 8.6:Multimedia human-machine interface and interaction
  8.6.1*:Human perception modelling
  8.6.2*:Modeling of multimodal perception
  8.6.3*:Human-human and human-computer dialog
  8.6.4*:Multimodal interfaces
  8.6.5*:Brain-computer interfaces
 8.7:Quality Assessment
  8.7.1*:Subjective visual quality assessment
  8.7.2*:Objective visual quality assessment
  8.7.3*:Subjective auditory quality assessment
  8.7.4*:Objective auditory quality assessment
  8.7.5*:Evaluation of user experience, cross-modal assessment
  8.7.6*:Standardization activities
 8.8:Multimedia databases and digital libraries
  8.8.1*:Visual indexing, analysis and representation
  8.8.2*:Audio indexing, analysis and representation
  8.8.3*:Content-based and context-based information retrieval
  8.8.4*:Knowledge and semantics in media annotation and retrieval
  8.8.5*:Fingerprinting and duplicate detection
 8.9:Multimedia computing systems and applications
  8.9.1*:Multimedia system design
  8.9.2*:Distributed multimedia systems
  8.9.3*:Entertainment and gaming
  8.9.4*:e-Health and telemedicine
  8.9.5*:IP video/web conferencing
 8.10:Hardware and software for multimedia systems
  8.10.1*:Multimedia hardware design
  8.10.2*:Real-time multimedia systems
  8.10.3*:Implementations on graphics processing units (GPUs)
  8.10.4*:Implementations on general-purpose processors, multimedia processors, DSPs, multi-core processors
  8.10.5*:Implementations in portable/wearable systems
  8.10.6*:Power-aware systems for multimedia
 8.11:Haptic technology and interaction
  8.11.1*:Processing and rendering of haptic signals
  8.11.2*:Compression and transmission of haptic signals
  8.11.3*:Audio-visual-haptic environments
  8.11.4*:Multimedia applications using haptics
 8.12*:Perceptual and bio-inspired multimedia systems and signal processing
  8.12.1*:Perceptual bio-inspired signal processing for multimedia
  8.12.2*:Multimodal signal fusion in humans and animals
  8.12.3*:Joint perceptual, bio-inspired and conventional multimedia signal processing
 8.13:Other multimedia applications
  8.13.1*:Multimedia authoring and composition
  8.13.3*:Multimedia applications using Crowdsourcing
  8.13.4*:Multimedia signal processing for robotics and automation
  8.13.5*:Social, mobile, and Internet of things media Processing
9:Sensor Array and Multichannel Signal Processing
 9.1:Sensor Array Processing
  9.1.2*:Physics-based sensor array processing
  9.1.3*:Inverse methods
  9.1.4*:Array calibration methods
  9.1.5*:Synthetic aperture methods
  9.1.6*:Signal detection and parameter estimation
  9.1.7*:Direction-of-arrival estimation
  9.1.8*:Source/target localization, classification, and tracking
  9.1.9*:Source separation and channel identification
 9.2:Adaptive Array Signal Processing
  9.2.1*:Adaptive beamforming
  9.2.2*:Space-time adaptive processing
  9.2.3*:MIMO radar and waveform diversity
  9.2.4*:Computational advances in array processing
 9.3:Multi-channel Signal Processing
  9.3.1*:Channel modeling and equalization
  9.3.2*:Multi-channel transceiver design
  9.3.3*:Sparsity structures in multichannel signal processing
  9.3.4*:Multi-channel processing with non-wave based sensors
  9.3.5*:Tensor-based signal processing for multi-sensor systems
 9.4:Multi-antenna and Multi-channel Signal Processing for Communications
  9.4.1*:MIMO systems and algorithms
  9.4.2*:Space-time coding and decoding algorithms
  9.4.3*:MIMO space-time code design and analysis
  9.4.4*:Multi-user MIMO networks
  9.4.5*:Array processing for wireless communications
  9.4.6*:Multi-antenna/multi-channel processing for cognitive radios
  9.4.7*:Massive MIMO array processing
 9.5:Sensor and Relay Networks
  9.5.1*:Sensor and relay network signal processing
  9.5.2*:Network beamforming and coding
  9.5.3*:Distributed processing and optimization, cooperative algorithms
  9.5.4*:Data fusion and decision fusion from multiple sensor types
  9.5.5*:Multi-Sensor processing for smart grid and energy systems
  9.5.6*:Network agent activity monitoring
  9.5.7*:Wireless acoustic sensor networks
  9.5.8*:Graph signal processing for sensor networks
 9.6:Applications of Sensor Array and Multi-channel Signal Processing
  9.6.1*:Radar array processing
  9.6.2*:Sonar array processing
  9.6.3*:Microphone array processing
  9.6.4*:Hyperspectral processing and unmixing
  9.6.5*:Integrated multi-model sensing
  9.6.6*:Super-resolution sensing and reconstruction
  9.6.7*:Multi-channel imaging
  9.6.8*:Multi-channel biological and medical modeling and processing
  9.6.9*:Sensor array applications of compressive sensing
  9.6.10*:Machine learning and fusion techniques
  9.6.11*:Other applications of SAM signal processing
10:Signal Processing for Communications and Networking
 10.1:Signal Transmission and Reception
  10.1.1*:Signal detection, estimation, separation and equalization
  10.1.2*:Channel modeling and estimation, training schemes
  10.1.3*:Capacity and performance analysis/optimization
  10.1.4*:Acquisition, synchronization and tracking
  10.1.5*:Signal representation, modulation, coding and compression
  10.1.6*:Joint source-channel coding and quantization
  10.1.7*:Demodulation and decoding
  10.1.8*:Compensation of hardware impairments
  10.1.9*:Sparse signal processing for communications
  10.1.10*:Ultra-reliable communications
  10.1.11*:Low latency communications
  10.1.12*:Millimeter wave (mmWave) and extremely high-frequency band
 10.2:Communication Systems and Applications
  10.2.1*:Multi-carrier, OFDM, and DMT communication
  10.2.2*:CDMA and spread spectrum communication
  10.2.3*:Ultra-wideband communication
  10.2.4*:Telephone networks, DSL and powerline communication
  10.2.5*:Applications involving signal processing for communication
  10.2.6*:Underwater communication systems
  10.2.7*:Free-space optical communication
  10.2.8*:Physical layer security
  10.2.9*:Energy harvesting in communication systems
  10.2.10*:Machine-type communications and Internet of Things (IoT)
  10.2.11*:Machine learning for communications and networking
 10.3:MIMO and Multi-User MIMO Communications and Signal Processing
  10.3.1*:MIMO precoder/decoder design, receiver algorithms
  10.3.2*:MIMO channel estimation and equalization
  10.3.3*:MIMO capacity and performance analysis
  10.3.4*:Space-time coding
  10.3.5*:MIMO multi-user and multi-access schemes
  10.3.6*:Massive MIMO
  10.3.7*:Random access and massive connectivity
 10.4:Communication Networks
  10.4.1*:Cooperative and cooordinated multi-cell techniques
  10.4.2*:Interference management techniques
  10.4.3*:Power and resource allocation
  10.4.4*:Energy management
  10.4.5*:Relaying and cooperative networks
  10.4.6*:Spectrum sensing
  10.4.7*:Cognitive radio and dynamic spectrum access
  10.4.8*:Heterogeneous networks
  10.4.9*:Ad-hoc networks
  10.4.10*:Network coding
  10.4.11*:Scheduling and queuing protocols
  10.4.12*:Optimization of communication networks
 10.5:Communication and sensing aspects of other networks
  10.5.1*:Distributed estimation and consensus
  10.5.2*:Collaborative signal processing
  10.5.3*:Distributed channel and source coding,
  10.5.4*:Collaborative signal processing for smart grid
  10.5.5*:Computation, communication, and control for smart grid
  10.5.6*:Communication/networking issues in social networks
  10.5.7*:Computation, communication, and control for biological networks
  10.5.8*:Network science
11:Signal Processing Theory and Methods
 11.1:Sampling and Reconstruction
  11.1.1*:Sampling theory and methods
  11.1.3*:Compressed and nonuniform sampling
  11.1.4*:Signal reconstruction, restoration, and enhancement
 11.2:Signal and System Modeling and Estimation
  11.2.1*:System and signal modeling: Theory, performance analysis
  11.2.2*:System identification and approximation
  11.2.3*:Non-stationary signals and time-varying systems
  11.2.4*:Time-frequency and time-scale analysis
 11.3:Statistical Signal Processing
  11.3.1*:Detection theory and methods
  11.3.2*:Estimation theory and methods
  11.3.3*:Classification and pattern recognition
  11.3.4*:Performance analysis and bounds
  11.3.5*:Robust methods
  11.3.6*:Signal separation methods
  11.3.7*:Bayesian signal processing
  11.3.8*:Tracking algorithms
 11.4:Adaptive Signal Processing
  11.4.1*:Adaptive filter analysis and design
  11.4.2*:Adaptive processing on networks
  11.4.3*:Applications of adaptive filters
 11.5:Nonlinear Systems and Signal Processing
  11.5.1*:Nonlinear systems and signal processing
  11.5.2*:Polynomial and kernel methods for Signal Processing
 11.6:Digital and Multirate Signal Processing
  11.6.1*:Multiresolution analysis, filter banks, and wavelets
  11.6.2*:Fast algorithms and transforms
 11.7:Signal Processing Over Graphs
  11.7.1*:Statistical approaches (models, etc.)
  11.7.2*:Deterministic approaches (graph filtering, graph transforms, etc.)
  11.7.3*:Graph representations and analysis
  11.7.4*:Adaptation and learning over graphs
 11.8: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
 11.9:Optimization Tools
  11.9.1*:Convex optimization and relaxation
  11.9.2*:Non-convex methods
  11.9.3*:Sparse and distributed optimization
 11.10*:Signal Processing on Networks
  11.10.1:Distributed processing and optimization
  11.10.2*:Social networks, social learning models
  11.10.3*:Estimation, detection and learning on networks
 11.11*:Quantum signal processing
12:Speech Processing
 12.1:Speech Production
  12.1.1*:Models of speech production
  12.1.2*:Models of disordered speech
  12.1.3*:Singing and properties of the musical voice
 12.2:Speech Perception and Psychoacoustics
  12.2.1*:Models of Speech Perception
  12.2.2*:Hearing and Psychoacoustics
  12.2.3*:Models of multimodal speech processing
  12.2.4*:Hearing assistive technology for speech
 12.3:Speech Analysis
  12.3.1*:Spectral and other time-frequency analysis techniques
  12.3.2*:Pitch/fundamental frequency analysis
  12.3.3*:Timing/duration/speaking rate analysis
  12.3.4*:Acoustic-phonetic features (e.g., formants etc.)
  12.3.5*:Extraction of non-linguistic information (e.g., gender, emotion, etc.)
  12.3.6*:Speaker physiological traits and voice quality
  12.3.7*:Voice, speech, and language disorders
  12.3.8*:Emotions and other non-verbal communication cues
  12.3.9*:Sentiment analysis, opinion mining, and social signal processing
 12.4:Speech Synthesis and Generation, including TTS
  12.4.1*:Machine learning for speech synthesis
  12.4.2*:Signal processing for speech synthesis
  12.4.3*:Text processing for speech synthesis
  12.4.4*:Systems, tools, and resources for speech synthesis
  12.4.5*:Quality assessment/evaluation metrics in speech synthesis
  12.4.6*:Prosody, emotion, and expression in speech synthesis
  12.4.7*:Multilingual and cross-lingual speech synthesis
  12.4.8*:Multi-modal speech synthesis
  12.4.9*:Articulatory synthesis
  12.4.10*:Voice transformation
 12.5:Speech Coding
  12.5.1*:Narrow-band and wide-band Speech Coding
  12.5.2*:Theory and techniques for signal coding (e.g., waveform, transform)
  12.5.3*:Unified speech and audio coding
  12.5.4*:Speech transmission system
  12.5.5*:Quality assessment/evaluation metrics (e.g., PESQ) in coding
 12.6:Speech Enhancement
  12.6.1*:Control and reduction of channel noise (e.g., reverb, room response)
  12.6.2*:Perceptual enhancement of non-noisy speech
  12.6.3*:Speech enhancement for humans with hearing impairments
  12.6.4*:Non-acoustic microphones for enhancement
  12.6.5*:Bandwidth expansion
  12.6.6*:Speech denoising, extraction, and separation
  12.6.7*:Multi channel enhancement (e.g., beamforming)
  12.6.8*:Model for enhancement (e.g., matrix and tensor factorization)
  12.6.9*:(Deep) learning based enhancement
 12.7:Acoustic Modeling for Automatic Speech Recognition
  12.7.1*:Feature Extraction
  12.7.2*:Feature representation learning
  12.7.3*:Novel network architecture
  12.7.4*:Pronunciation modeling at the acoustic level
  12.7.5*:State clustering and novel state definitions
  12.7.6*:Prosody and other speech characteristics
  12.7.7*:Dialect, accent, and idiolect at the acoustic level
  12.7.8*:Sequence discriminative training
  12.7.9*:Optimization methods
  12.7.10*:Articulatory and physiological modeling
  12.7.11*:Feature Transformation and Normalization
 12.8:Robust Speech Recognition
  12.8.1*:Features specifically for robust ASR (noise, channel, etc.)
  12.8.2*:Data generation for matched/multi conditions
  12.8.3*:Speech recognition with microphone arrays
  12.8.4*:Model/backend based robust ASR
  12.8.5*:Integration of speech enhancement and recognition
  12.8.6*:Confidence measures and rejection
  12.8.7*:Speech Activity/End-point/Barge-in detection
 12.9:Speech Adaptation/Normalization
  12.9.1*:Speaker adaptation and normalization (e.g., VTLN)
  12.9.2*:Speaker adapted training methods
  12.9.3*:Environmental/Channel adaptation
  12.9.4*:Register, dialect, idiolect adaptation
 12.10:General Topics in Speech Recognition
  12.10.1*:Distributed Speech Recognition - Client/Server methods
  12.10.2*:Computational efficiency for GPU/FPGA/other hardware usage
  12.10.3*:Alternative Statistical/Machine Learning Methods (e.g., no HMMs, DNNs)
  12.10.4*:Word spotting
  12.10.5*:Metadata (e.g., emotion, speaker, accent) extraction from acoustics
  12.10.6*:New algorithms, computational strategies, data- structures for ASR
  12.10.7*:Multi-modal (such as audio-visual) speech recognition
  12.10.8*:Corpora, annotation, and other resources
  12.10.9*:Algorithm approximation methods in ASR
  12.10.10*:Structured classification approaches
 12.11:Multilingual Recognition and Identification
  12.11.1*:Language ID and dialect identification
  12.11.2*:Language, dialect, and accent identification
  12.11.3*:Multilingual speech processing and recognition
 12.12:Lexical Modeling and Access
  12.12.1*:Pronunciation modeling at the lexical level
  12.12.2*:Dialect, accent, and idiolect at the lexical level
  12.12.3*:Multilingual aspects (e.g., unit selection)
  12.12.4*:Automatic lexicon learning
 12.13:Large Vocabulary Continuous Recognition/Search
  12.13.1*:Decoding algorithms and implementation
  12.13.3*:Multi-pass strategies
  12.13.4*:Miscellaneous Topics
  12.13.5*:End-to-end speech recognition
 12.14:Speaker Recognition and Characterization
  12.14.1*:Features and characteristics for speaker recognition
  12.14.2*:Robustness to variable and degraded channels
  12.14.3*:Speaker verification and identification
  12.14.4*:Segmentation and clustering
  12.14.5*:Speaker characterization and adaptation
  12.14.6*:Corpora, annotation, evaluation, tools, and other resources
  12.14.7*:Speaker diarization (time) and speaker localization (space)
 12.15:Resource constrained speech recognition
  12.15.1*:Low-power and reduced computation speech recognition
  12.15.2*:ASR techniques for highly portable/mobile devices and embedded systems
13:Human Language Technology
 13.1:Spoken Language Understanding
  13.1.1*:Machine learning for understanding
  13.1.2*:Semantic classification
  13.1.3*:Entity extraction from speech
  13.1.4*:Spoken document summarization and topic modeling
  13.1.5*:Question/answering from speech
  13.1.6*:Paralinguistic (emotion, age, gender, rate, etc.) and nonlinguistic information
  13.1.7*:Detecting linguistic/discourse structure (e.g., disfluencies, sentence/topic boundaries, speech acts)
 13.2:Human Spoken Language Acquisition, Development and Learning
  13.2.1*:Language acquisition, development, and learning models
  13.2.3*:Attributes and modeling techniques for assessment of language fluency
  13.2.3*:Computer assisted pronunciation training, computer assisted language learning
 13.3:Spoken and Multimodal Dialog Systems and Applications
  13.3.1*:Spoken and multimodal dialog systems, applications, and architectures
  13.3.2*:Machine learning and neural methods for dialog modeling
  13.3.3*:Response Generation
  13.3.4*:Evaluation metrics and standards
  13.3.5*:Speech/voice-based human-computer interfaces (HCI)
 13.4:Machine Translation of Speech
  13.4.1*:Speech processing for machine translation
  13.4.2*:Para-linguistic and non-linguistic processing for machine translation
  13.4.3*:Corpora, annotation, tools, and other resources
  13.4.4*:Integration of speech and linguistic processing
  13.4.5*:Machine translation for named entities
  13.4.6*:Machine translation for code-switching speech
  13.4.7*:Machine translation for multi-lingual dialogue
  13.4.8*:Incremental processing for machine translation
  13.4.9*:Evaluation metrics (e.g., BLEU)
  13.4.10*:Systems and applications for MTS
  13.4.11*:End-to-end machine translation of speech
 13.5:Language Modeling, for Speech and HLT
  13.5.1*:N-grams, their generalizations and smoothing methods.
  13.5.2*:Language Model Adaptation
  13.5.3*:Dialect, accent, and idiolect at the language level
  13.5.4*:Discriminative LM Training Methods
  13.5.5*:Representation learning approaches to LM, e.g., deep learning
  13.5.6*:Other approaches to LMs
 13.6:Speech Retrieval
  13.6.1*:Spoken keyword search (search term is text)
  13.6.2*:Spoken audio search or query-by-example spoken-term detection (search query is audio)
  13.6.3*:Search and retrieval of spoken documents
 13.7:Data mining and language resources
  13.7.1*:Speech data mining
  13.7.2*:General corpora, annotation, tools and other resources
14:Signal Processing for Big Data
 14.1:Computational models and representations for big data
  14.1.1*:Compressive sampling for big data
  14.1.2*:Tensor factorization models for multi-way data
  14.1.3*:Randomized linear algebra for big data
  14.1.4*:Scalable (fast)
  14.1.5*:Spectral decompositions for representation of big data
  14.1.6*:Graph signal processing theory
  14.1.7*:Transforms for graph signals
  14.1.8*:Graph simplification and multi-resolution methods
 14.2:Big data acquisition, storage, retrieval, interpretation
  14.2.1*:Protocols for networked storage, indexing and retrieval
  14.2.2*:Signal processing hardware and architectures for massive datasets
  14.2.3*:Data resiliency to node failure
  14.2.4*:Lossless data compression for massive datasets
  14.2.5*:Lossy data compression for massive datasets
  14.2.6*:Sketching, streaming, and real time data retrieval for time varying (spatio-temporal) data
 14.3:Learning and inference with big data
  14.3.1*:High dimensional spatio-temporal models
  14.3.2*:Theoretical limits of high dimensional statistical inference
  14.3.3*:Methods of anomaly/change detection with time varying big data
  14.3.4*:Random matrix models and non-commutative information theory for big data
  14.3.5*:Statistical modeling of heterogeneous data types
  14.3.6*:Learning correlation networks for big data
  14.3.7*:Learning Bayes networks for big data
  14.3.8*:Deep learning for big data
  14.3.9*:Non-parametric learning for big data
  14.3.10*:Crowdsourcing/human computation for big data processing
  14.3.11*:Stream Mining and Decision Making from Big Data
  14.3.12*:Signal processing approaches to discovery/creativity/machine science
 14.4:SP Methods for Big Data Analytics
  14.4.1*:Visualization and summarization of big data
  14.4.2*:Social media, recommendation systems and collaborative filtering
  14.4.3*:Defense, intelligence and security
  14.4.4*:Biology and medicine
  14.4.5*:Astronomy and other physical sciences
  14.4.6*:Urban informatics
  14.4.7*:Big Data in Social sciences
  14.4.8*:Business analytics, forensics and finance
 14.5:Distributed signal and information processing for big data on networks
  14.5.1*:Distributed processing over heterogeneous networks
  14.5.2*:Distributed processing over time-varying networks
  14.5.3*:Distributed algorithms for processing big data on networks
  14.5.4*:Distributed detection and estimation
  14.5.5*:Distributed control, optimization and learning
  14.5.6*:Distributed storage, indexing and retrieval
15:Signal Processing for Internet of Things
 15.1:IOT Communication and networking
  15.1.1*:Signal processing issues in IoT with 5G and beyond
  15.1.2*:mm-wave based IoT systems
  15.1.3*:Wired and wireless M2M Communication and networking
  15.1.4*:Energy-efficient communication
  15.1.5*:Spectrum efficiency and management for IoT
 15.2:IOT Information Processing
  15.2.1*:Low-power, Distributed data processing on sensors
  15.2.2*:Intelligent signal and information processing
  15.2.3*:Data mining
  15.2.4*:Information fusion
  15.2.5*:Processing algorithms and theories for IoT
  15.2.6*:Cross-layer processing for IoT
 15.3:IOT Security
  15.3.1*:Cyber security for IOT
  15.3.2*:Reliability for IOT
  15.3.3*:Privacy perseverance and trust for IOT
 15.4:IOT Systems
  15.4.1*:Human-cyber-physical interaction
  15.4.2*:Sensing, networking and computing with smartphones and wearable devices
  15.4.3*:Computing and processing platforms for IoT Implementations
  15.4.4*:Programing and Developing Tools to enable IoT
  15.4.5*:Quality of Experiences and Quality of Services in IoT applications
  15.4.6*:Prototypes, test-beds and field trials on smart services and applications, e.g., Smart cities, intelligent transportation, building automation, assisted living, e-health, etc
16:Signal Processing (SP) Education
 16.1*:Novel laboratory, computer-based, and distance teaching methods
 16.2*:New SP pedagogy, including MOOCs and flipped classes
 16.3*:New technologies in SP education
 16.4*:SP across the engineering curriculum
 16.5*:SP curriculum issues (early/late, simulation/real-time, theory/practice)
 16.6*:Industry and SP education: Linking academic knowledge with industrial needs
 16.7*:Educational practices on signal acquisition, conditioning, and other analog issues
 16.8*:Teaching embedded systems and other hardware for processing signals
 16.9*:SP outreach programs
 16.10*:Education strategies to encourage participation of women and underrepresented students in SP careers
 16.11*:SP tips and tricks
17:Computational Imaging
 17.1:IMT Computational Imaging Methods and Models
  17.1.1*:IMT-CIS Coded Image Sensing
  17.1.2*:IMT-CST Compressed Sensing
  17.1.3*:IMT-SIM Statistical Image Models
  17.1.4*:IMT-SLM Sparse and Low Rank Models
  17.1.5*:IMT-GIM Graphical Image Models
  17.1.6*:IMT-LBM Learning-Based Models
  17.1.7*:IMT-PIM Perceptual Image Models
 17.2:CIF Computational Image Formation
  17.2.1*:CIF-SBR Sparsity-Based Reconstruction
  17.2.2*:CIF-SBI Statistically-Based Inversion
  17.2.3*:CIF-MIF Multi-Image & Sensor Fusion
  17.2.4*:CIF-OBI Optimization-based Inversion Methods
  17.2.5*:CIF-MLI Machine Learning based Computational Image Formation
 17.3:CIS Computational Imaging Systems
  17.3.1*:CIS-CPH Computational Photography
  17.3.2*:CIS-MIS Mobile Imaging
  17.3.3*:CIS-PIS Pervasive Imaging
  17.3.4*:CIS-HCC Human Centric Computing
  17.3.5*:CIS-CMI Computational Microscopy
  17.3.6*:CIS-SSI Spectral Sensing
  17.3.7*:CIS-TIM Tomographic Imaging
  17.3.8*:CIS-MRI Magnetic Resonance Imaging
  17.3.9*:CIS-AIM Acoustic Imaging
  17.3.10*:CIS-RIM Radar Imaging
  17.3.11*:CIS-NCI Novel Computational Imaging Systems
  17.3.12*:CIS-NLC Non-Linear Computational Imaging Systems
 17.4:HSS Computational Imaging Hardware and Software
  17.4.1*:HSS-HPC High-performance embedded computing systems
  17.4.2*:HSS-BDC Big Data Computational Imaging
  17.4.3*:HSS-HDD Integrated Hardware/Digital Design
  17.4.4*:HSS-NSS Non-traditional Sensor Systems