Paper Topics

1 Applied Signal Processing Systems
1.1: Algorithm and architecture design and synthesis
1.2: Signal processing hardware
1.3: Signal processing software
1.4: Signal processing systems
1.5: Signal processing over IoT
1.6: Emerging topics in signal processing systems
2 Audio and Acoustic Signal Processing
2.1: Modeling, analysis and synthesis of acoustic environments
2.2: Detection and classification of acoustic scenes and events
2.3: Auditory modeling and hearing instruments
2.4: Acoustic sensor array processing
2.5: Active noise control, echo reduction and feedback reduction
2.6: System identification and reverberation reduction
2.7: Audio and speech source separation
2.8: Audio signal enhancement and restoration
2.9: Audio and speech quality and intelligibility measures
2.10: Spatial audio recording and reproduction
2.11: Audio and speech modeling, coding and transmission
2.12: Music signal analysis, processing and synthesis
2.13: Music information retrieval and music language processing
2.14: Audio for multimedia and audio processing systems
2.15: Bioacoustics and medical acoustics
2.16: Audio security
3 Biomedical Imaging and Signal Processing
3.1: Medical image reconstruction and restoration
3.2: Medical image detection and estimation
3.3: Medical image registration and motion analysis
3.4: Medical image feature extraction and fusion
3.5: Biological image analysis
3.6: Physiological signal processing (ECG, EEG, MEG)
3.7: Brain/human-computer interfaces
3.8: Bioinformatics
4 Computational Imaging
4.1: Computational imaging methods and models
4.2: Compressed sensing
4.3: Learning-based computational imaging models
4.4: Computational image formation
4.5: Computational imaging systems
4.6: Computational photography
5 Image, Video, and Multidimensional Signal Processing
5.1: Image and video sensing and acquisition
5.2: Image and video representation
5.3: Perception and quality models for images and video
5.4: Signal processing for images and video modeling
5.5: Biomedical and biological image processing
5.6: Image and video coding
5.7: Imaging and video networks
5.8: Image and video processing for watermarking and security
5.9: Multimedia communications
5.10: Scanned, color, and multispectral imaging and processing
5.11: Stereoscopic and multiview processing, display and coding
5.12: Hardware and software systems for image and video processing
5.13: 3D image and video processing and analysis
5.14: Image and video processing augmented and virtual reality
5.15: Image and video content analysis
5.16: Image and video storage and retrieval
5.17: Machine learning for image processing
5.18: Image and video synthesis, rendering, and visualization
6 Information Forensics and Security
6.1: Watermarking and data hiding
6.2: Biometrics
6.3: Communication and information theoretic security
6.4: Multimedia forensics
6.5: Multimedia content hash
6.6: Adversarial machine learning
6.7: Applied cryptography
6.8: Anonymization and data privacy
6.9: Cybersecurity
6.10: Hardware security
6.11: Network security
6.12: System security
6.13: Surveillance
6.14: Usability and human factors
6.15: Other forensics and security-related topics
7 Machine Learning for Signal Processing
7.1: Deep learning techniques
7.2: Deep generative models
7.3: Self-supervised and semi-supervised learning
7.4: Transfer learning
7.5: Adversarial machine learning
7.6: Distributed/Federated learning
7.7: Graph neural networks
7.8: Reinforcement learning
7.9: Learning theory and algorithms
7.10: Bounds on performance
7.11: Graphical and kernel methods
7.12: Matrix factorizations/completion
7.13: Dictionary learning
7.14: Source separation
7.15: Independent component analysis
7.16: Sparsity-aware processing
7.17: Subspace and manifold learning
7.18: Tensor-based signal processing
7.19: Cognitive information processing
7.20: Information-theoretic learning
7.21: Pattern recognition and classification
7.22: Feature extraction/selection/learning
7.23: Applications of machine learning
7.24: Applications in music and audio processing
7.25: Applications in time series analysis
7.26: Learning from multimodal data
7.27: Sequential learning; sequential decision methods
7.28: Machine learning over wireless networks
7.29: Big data
8 Multimedia Signal Processing
8.1: Multi-modal signal processing and analysis (audio/visual/haptics/radar/lidar etc.)
8.2: Multimedia analysis and synthesis
8.3: Multimedia compression, coding, conversion, and transcoding
8.4: Multimedia standardization
8.5: Human-centric multimedia and human-machine interaction
8.6: Immersive multimedia technologies and applications
8.7: Quality of experience
8.8: Multimedia databases and information retrieval
8.9: Multimedia communications and streaming
8.10: Distributed multimedia and Internet-of-Things
8.11: Multimedia perception and processing for autonomous systems
8.12: Multimedia in healthcare, education, art, and social sciences
8.13: Machine/deep learning methodologies for multimedia
9 Sensor Array and Multichannel Signal Processing
9.1: Beamforming
9.2: Direction of arrival estimation and source Localization
9.3: Source separation/inverse methods and parameter estimation
9.4: Target detection, classification, and tracking
9.5: Array calibration, performance analysis and bounds
9.6: Compressed sensing and sparse modelling for multi-sensor systems
9.7: Multichannel processing and space-time adaptive methods
9.8: Non-wave based array processing
9.9: Tensor processing for multi-sensor systems
9.10: Computational advances for multi-sensor systems
9.11: Learning models and methods for multi-sensor systems
9.12: Acoustic and microphone array processing
9.13: MIMO radar and waveform design
9.14: MIMO and massive MIMO communication systems
9.15: Sensor networks and graph signal processing
9.16: Integrated sensing and communication
9.17: Geophysical and seismic signal processing
9.18: Sensor arrays for medical signal and image processing
9.19: Multi-sensor and synthetic aperture sensing and imaging
9.20: Other applications of sensor and array multichannel processing
10 Signal Processing for Communications and Networking
10.1: Signal modulation, demodulation, encoding and decoding
10.2: Channel modelling and estimation
10.3: Sparse SP techniques for communication
10.4: Machine learning for communications
10.5: Distributed/federated learning
10.6: Information theory and performance bounds
10.7: Compensation and calibration of front end components
10.8: Interference management techniques
10.9: Multi-carrier and spread spectrum techniques
10.10: Multiple-input multiple-output communication systems
10.11: High frequency and ultra wideband communication
10.12: Low latency communications
10.13: Networks and network resource allocation
10.14: Sensor and ad-hoc networks
10.15: Distributed, adaptive, and collaborative communication techniques
10.16: Energy efficiency in communications
10.17: Applications of signal processing for communications
10.18: Physical layer security
10.19: Optical wireless communications
10.20: Signal processing for non-terrestrial communications
10.21: Integrated sensing and communication
11 Signal Processing Theory and Methods
11.1: Sampling theory, compressed and non-uniform sampling
11.2: Signal filtering, restoration, enhancement, and reconstruction
11.3: Multiresolution analysis, filter banks, and wavelets
11.4: Signal and information processing over graphs
11.5: Optimization methods for signal processing
11.6: Quantum signal processing
11.7: Adaptive signal processing
11.8: Detection, classification
11.9: Estimation theory and methods
11.10: Linear and non-linear systems and signal processing
11.11: Bayesian signal processing
11.12: Tracking
11.13: Signal processing over networks
11.14: Sparse/low-dimensional signal recovery, parameter estimation and regression
11.15: Structured matrix factorization, low-rank models, matrix completion
11.16: Dictionary learning, subspace and manifold learning
12 Speech and Language Processing
12.1: Speech production, perception and psychoacoustics
12.2: Speech emotion detection and analysis
12.3: Speech analysis and Language disorder Analysis
12.4: Speech and singing voice synthesis/convertion/coding
12.5: Speech enhancement and separation
12.6: Acoustic modeling for automatic speech recognition
12.7: Robust speech recognition and adaptation
12.8: Resource constrained speech recognition
12.9: Large vocabulary continuous speech recognition/search
12.10: Multilingual speech recognition and identification
12.11: New algorithms and approaches for speech recognition
12.12: Word spotting, VAD, and other topics in speech recognition
12.13: Speaker recognition/identification/diarization
12.14: Speaker verification and anti-spoofing
12.15: Language modeling
12.16: Machine translation for spoken and written language
12.17: Language understanding and computational semantics
12.18: Discourse and dialog
12.19: Spoken document retrieval and written text mining
12.20: Segmentation, tagging, and parsing
12.21: Language acquisition and learning
12.22: Machine learning methods for language
12.23: Language resources and systems
12.24: Multimodal processing of language
13 Signal Processing Education
13.1: Resources and tools for teaching signal processing
. 13.2: Novel pedagogical approaches in signal processing (graduate, undergraduate, K-12, continuing education)
13.3: Case study reports in signal processing education