Paper Topics
1: Signal Processing Theory and Methods [TM]
1.1: Digital signal processing [TM-DSP]
1.1.1: Signal and system modeling [TM-DSP-SMOD]
1.1.2: Sampling theory [TM-DSP-SAMP]
1.1.3: Transforms [TM-DSP-TRSF]
1.1.4: Filtering [TM-DSP-FILT]
1.1.5: Time-frequency and multiresolution analysis [TM-DSP-TFSR]
1.2: Statistical signal processing [TM-SSP]
1.2.1: Estimation [TM-SSP-ESTI]
1.2.2: Detection and classification [TM-SSP-DETC]
1.2.3: Bayesian signal processing [TM-SSP-BAYE]
1.2.4: Sparse and low-dimensional signal recovery [TM-SSP-SPAR]
1.3: Matrix and tensor methods [TM-MAT]
1.3.1: Matrix and tensor factorization and completion [TM-MAT-FACT]
1.3.2: Sparse and non-negative matrices and tensors [TM-MAT-SPAR]
1.3.3: Source separation [TM-MAT-SSEP]
1.3.4: Independent component analysis [TM-MAT-ICA]
1.3.5: Subspace and manifold learning [TM-MAT-SUBS]
1.3.6: Tensor-based signal processing [TM-MAT-TENS]
1.4: Adaptive signal processing [TM-ASP]
1.4.1: Tracking [TM-ASP-TRAC]
1.5: Optimization methods [TM-OPT]
1.5.1: Distributed optimization [TM-OPT-DIST]
1.5.2: Sparse optimization [TM-OPT-SPAR]
1.6: Graph signal processing [TM-GSP]
1.7: Distributed signal processing theory and methods [TM-DIS]
1.7.1: Distributed optimization [TM-DIS-OPT]
1.8: Distributed signal processing theory and methods [TM-APP]
2: Machine Learning for Signal Processing [ML]
2.1: Machine learning paradigms [ML-LEA]
2.1.1: Supervised and semi-supervised learning [ML-LEA-SUPE]
2.1.2: Unsupervised learning [ML-LEA-UNSU]
2.1.3: Self-supervised learning [ML-LEA-SSUP]
2.1.4: Adversarial machine learning [ML-LEA-ADVR]
2.1.5: Causal learning [ML-LEA-CAUS]
2.1.6: Quantum machine learning [ML-LEA-QUAN]
2.2: Reinforcement learning [ML-REI]
2.3: Deep learning [ML-DLR]
2.3.1: Deep learning models [ML-DLR-MODL]
2.3.2: Deep learning training methods [ML-DLR-METH]
2.3.3: Deep generative models [ML-DLR-GENM]
2.3.4: Graph neural networks [ML-DLR-GNN]
2.3.5: Transfer learning and meta-learning [ML-DLR-TRAN]
2.3.6: Topological deep learning [ML-DLR-TOP]
2.3.7: Representation learning [ML-DLR-REPR]
2.5: Trustworthy and reliable machine learning [ML-TRD]
2.5.1: Robust and trustworthy machine learning [ML-TRD-ROBU]
2.5.2: Explainable and interpretable machine learning [ML-TRD-EXPL]
2.5.3: Distributed and federated learning [ML-TRD-DIS]
2.5.4: Fairness and privacy [ML-TRD-PRIV]
2.5.5: Sustainable machine learning [ML-TRD-SUST]
2.6: Conventional machine learning [ML-CON]
2.6.1: Pattern recognition and clustering [ML-CON-PATT]
2.6.2: Performance analysis and bounds [ML-CON-PERF]
2.6.3: Graphical and kernel methods [ML-CON-KERN]
2.6.4: Dictionary learning [ML-CON-DICT]
2.6.5: Information theoretic learning [ML-CON-INFO]
2.6.6: Bayesian machine learning [ML-CON-BAYE]
2.6.7: Sequential learning [ML-CON-SEQU]
2.6.8: Sparsity-aware learning [ML-CON-SPAR]
2.6.9: Feature extraction and selection [ML-CON-FEAT]
2.7: Applications and other topics of machine learning [ML-APP]
2.7.1: Machine learning for time series analysis [ML-APP-TIME]
2.7.2: Machine learning for creative arts [ML-APP-ART]
2.7.3: Machine learning for education [ML-APP-EDU]
2.7.4: Machine learning for sciences [ML-APP-SCI]
2.7.5: Emerging applications of machine learning [ML-APP-EMG]
3: Signal Processing for Sensing and Communication [SC]
3.1: Array signal processing [SC-SAM]
3.1.1: Beamforming and source separation [SC-SAM-BEAM]
3.1.2: Direction of arrival estimation and source localization [SC-SAM-DOAE]
3.1.3: Array calibration [SC-SAM-CALB]
3.1.4: Tracking [SC-SAM-TRCK]
3.1.5: Performance analysis and bounds [SC-SAM-PERF]
3.1.6: MIMO and massive MIMO array processing [SC-SAM-MIMO]
3.2: Signal processing for communication [SC-COM]
3.2.1: Signal modulation and encoding [SC-COM-MODU]
3.2.2: Signal detection, estimation, demodulation and decoding [SC-COM-DETC]
3.2.3: Channel modeling and estimation [SC-COM-CHAN]
3.2.4: Machine learning for communications [SC-COM-ML]
3.2.5: Information theory [SC-COM-INFO]
3.2.6: Physical layer security [SC-COM-PHYS]
3.2.7: MIMO and massive MIMO communication [SC-COM-MIMO]
3.2.8: Low latency communication [SC-COM-LATE]
3.2.9: Energy aware communication [SC-COM-ENGY]
3.3: Signal processing for networks and distributed systems [SC-NET]
3.3.1: Network resource management [SC-NET-RESC]
3.3.2: Edge, sensor and ad-hoc networks [SC-NET-ADHC]
3.3.3: Cooperative networking / cognitive radio [SC-NET-COOP]
3.4: Distributed processing [SC-DIS]
3.4.1: Distributed processing [SC-DIS-]
3.4.2: Machine learning over distributed networks [SC-DIS-ML]
3.5: Integrated sensing and communication [SC-ISC]
3.6: Remote sensing, radar and sonar signal processing [SC-RAS]
3.6.1: MIMO radar and waveform design [SC-RAS-MIMO]
3.7: Applications and other topics in signal processing for sensing and communication [SC-APP]
3.7.1: Sensor arrays for medical signal and image processing [SC-APP-MEDI]
3.7.2: Acoustic and microphone array processing [SC-APP-MICR]
3.7.3: Geophysical and seismic signal processing [SC-APP-GEOS]
3.7.4: Non-wave based array processing [SC-APP-NWAV]
3.7.5: Non-terrestrial communications [SC-APP-NTER]
3.7.6: Optical wireless communication [SC-APP-OPTI]
3.7.7: Quantum communication [SC-APP-QUAN]
3.7.8: Intelligent surfaces [SC-APP-SURF]
3.7.9: Other topics in signal processing for sensing and communication [SC-APP-OTHR]
4: Biomedical Signal and Image Processing [BI]
4.1: Medical imaging [BI-MED]
4.1.1: Medical image formation, reconstruction and restoration [BI-MED-FORM]
4.1.2: Medical image analysis [BI-MED-ANLS]
4.1.3: Multimodal medical image fusion and analysis [BI-MED-FUSE]
4.2: Biological imaging [BI-BIO]
4.2.1: Biological image formation, reconstruction and restoration [BI-BIO-FORM]
4.2.2: Biological image analysis [BI-BIO-ANLS]
4.3: Biomedical signal processing [BI-BSP]
4.3.1: Physiological and wearable signal processing [BI-BSP-PHYS]
4.3.2: Neural signals [BI-BSP-NEUR]
4.4: Brain/human-computer interfaces [BI-BCI]
4.5: Bioinformatics [BI-INF]
4.6: Applications and emerging methods in biomedical image and signal processing [BI-APP]
5: Image, Video and Multidimensional Signal Processing [IV]
5.1: Image and video sensing, modeling, and representation [IV-SMR]
5.1.1: Image and video sensing and acquisition [IV-SMR-ACQN]
5.1.2: Statistical-model based methods for image and video [IV-SMR-STAT]
5.1.3: Structural-model based methods for image and video [IV-SMR-STRC]
5.1.4: Image and video representation [IV-SMR-REPR]
5.1.5: Perception and quality models for images and video [IV-SMR-PERC]
5.2: Image and video processing techniques [IV-TEC]
5.2.1: Biomedical and biological image processing [IV-TEC-BIOM]
5.2.2: Machine learning for image and video processing [IV-TEC-ML]
5.3: Image and video communications [IV-COM]
5.3.1: Image and video coding [IV-COM-CODE]
5.3.2: Imaging and video communication networks [IV-COM-NETW]
5.3.3: Image and video processing for watermarking and security [IV-COM-SECU]
5.3.4: Image and video multimedia communications [IV-COM-MCOM]
5.3.5: Machine learning for image and video communication [IV-COM-ML]
5.4: Image and video analysis, synthesis, and retrieval [IV-ANA]
5.4.1: Image and video content analysis [IV-ANA-CONT]
5.4.2: Image and video storage and retrieval [IV-ANA-STOR]
5.4.3: Image and video synthesis, rendering, and visualization [IV-ANA-SYNT]
5.5: Three-dimensional image and video analysis and processing [IV-3D]
5.5.1: Stereoscopic and multiview processing, display and coding [IV-3D-STER]
5.6: Electronic imaging [IV-ELI]
5.6.1: Image scanning and capture [IV-ELI-SCAN]
5.6.2: Color and multispectral imaging [IV-ELI-COLR]
5.6.3: Scanned document analysis, processing, and coding [IV-ELI-ANLS]
5.6.4: Hardware and software systems for image and video processing [IV-ELI-HRDW]
5.7: Applications and other topics in image, video and multidimensional signal processing [IV-APP]
6: Computational Imaging [CI]
6.1: Computational imaging methods and models [CI-TEC]
6.1.1: Sparse, low-rank, and low-dimensional models for computational imaging [CI-TEC-SPAR]
6.1.2: Machine learning-based methods for computational imaging [CI-TEC-ML]
6.2: Computational imaging modalities [CI-MOD]
6.2.1: Computational photography [CI-MOD-COMP]
6.3: Computational imaging hardware and algorithms [CI-HWR]
6.4: Applications and other topics in computational imaging [CI-APP]
7: Multimedia Signal Processing [MM]
7.1: Multimedia creation and synthesis [MM-SYN]
7.1.1: Multimedia synthesis and rendering [MM-SYN-REND]
7.2: Multimedia architecture design and systems [MM-SYS]
7.2.1: Frugal and green multimedia [MM-SYS-ENGY]
7.3: Multi-modal processing, analysis and synthesis [MM-MOD]
7.3.1: Multi-modal signal processing and analysis [MM-MOD-ANLS]
7.3.2: Machine/deep learning methodologies for multimedia [MM-MOD-ML]
7.3.3: Generative/large multi-modal models [MM-MOD-GENM]
7.3.4: Multimedia understanding [MM-MOD-UNDE]
7.4: Multimedia compression, transmission and security [MM-COM]
7.6: Multimedia environments and user experience [MM-USR]
7.6.1: Immersive and 3D multimedia processing and coding [MM-USR-IMRS]
7.6.2: Quality of experience [MM-USR-QUAL]
7.7: Multimedia information retrieval and datasets [MM-DAT]
7.8: Applications in multimedia (healthcare, education, art, distributed multimedia, etc.) [MM-APP]
7.8.1: Multimedia perception and processing for autonomous systems [MM-APP-AUTO]
8: Information Forensics and Security [IF]
8.1: Applied cryptography [IF-CRY]
8.2: Watermarking and data hiding [IF-WAT]
8.3: Anonymization and data privacy [IF-PRV]
8.4: Multimedia forensics [IF-FOR]
8.5: Machine learning for information forensics and security [IF-ML]
8.5.1: Adversarial machine learning [IF-ML-ADVE]
8.6: Biometrics [IF-BIO]
8.7: Cybersecurity [IF-SEC]
8.7.1: Hardware security [IF-SEC-HRDW]
8.7.2: Network security [IF-SEC-NETW]
8.7.3: System security [IF-SEC-SYST]
8.7.4: Communication and information theoretic security [IF-SEC-COMM]
8.8: Surveillance [IF-SUR]
8.9: Applications and other topics in forensics and security [IF-APP]
9: Audio and Acoustic Signal Processing [AA]
9.1: Audio signal processing [AA-AUD]
9.1.1: Signal enhancement, restoration, and extraction [AA-AUD-SEN]
9.1.2: Audio and speech source separation [AA-AUD-SEP]
9.1.3: Audio and speech coding, transmission, and representations [AA-AUD-COMM]
9.1.4: Audio and speech quality and intelligibility measures [AA-AUD-QUAL]
9.1.5: Auditory modeling and hearing instruments [AA-AUD-MODL]
9.1.6: System identification and dereverberation [AA-AUD-IDEN]
9.1.7: Acoustic sensor array processing [AA-AUD-ARRY]
9.1.8: Fundamental theory and algorithms for audio and acoustic signal processing [AA-AUD-METH]
9.2: Acoustic scenes and events [AA-SCE]
9.2.1: Audio captioning, retrieval, and understanding [AA-SCE-CAPT]
9.2.2: Sound event and anomaly detection and sound scene classification [AA-SCE-DETC]
9.2.3: Sound generation and synthesis [AA-SCE-SYNT]
9.3: Acoustic environment processing [AA-ENV]
9.3.1: Modeling, analysis, and synthesis of acoustic environments [AA-ENV-CHAN]
9.3.2: Spatial audio recording and reproduction [AA-ENV-ACQN]
9.3.3: Active noise control; acoustic echo and feedback cancellation [AA-ENV-ECHO]
9.4: Music analysis, processing, and generation [AA-MUS]
9.4.1: Music analysis [AA-MUS-ANLS]
9.4.2: Music signal processing, production, and separation [AA-MUS-PROC]
9.4.3: Audio- and symbolic-domain music generation and content creation [AA-MUS-MGEN]
9.5: Applications and other topics in audio and acoustic signal processing [AA-APP]
9.5.1: Bioacoustics and medical acoustics [AA-APP-BIO]
9.5.2: Audio security [AA-APP-SEC]
9.5.3: Audio for video and multimedia [AA-APP-VIMM]
9.5.4: Data and open source for audio and acoustic signal processing [AA-APP-DATA]
10: Speech and Language Processing [SL]
10.1: Human language processing [SL-HLT]
10.1.1: Discourse and dialog [SL-HLT-DIAL]
10.1.2: Language understanding and computational semantics [SL-HLT-UNDE]
10.1.3: Spoken document retrieval and summarization [SL-HLT-SUMM]
10.1.4: Segmentation, tagging, and parsing of language [SL-HLT-PARS]
10.1.5: Summarization, retrieval and language learning [SL-HLT-LEAR]
10.1.6: Machine Learning for natural language processing [SL-HLT-ML]
10.1.7: Generation in natural language processing [SL-HLT-GEN]
10.1.8: Question answering [SL-HLT-QUES]
10.2: Multi-modal/cross-modal speech and language processing [SL-MM]
10.3: Speaker recognition, identification and verification [SL-SPR]
10.3.1: Speaker diarization and identification [SL-SPR-IDEN]
10.3.2: Speaker verification [SL-SPR-VERI]
10.3.3: Speaker anti-spoofing [SL-SPR-SPFG]
10.4: Speech enhancement and extraction [SL-ENH]
10.5: Speech event detection [SL-EVT]
10.5.1: Speech emotion recognition [SL-EVT-EMO]
10.6: Speech generation [SL-GEN]
10.6.1: Speech/singing voice conversion and cloning [SL-GEN-CONV]
10.6.2: Text-to-speech generation [SL-GEN-TEXT]
10.6.3: Neural vocoder and codec [SL-GEN-CODE]
10.6.4: Audio/music/singing voice generation [SL-GEN-MUSI]
10.6.5: Watermarking and anti-spoofing [SL-GEN-SPFG]
10.7: Speech processing resources [SL-DAT]
10.8: Speech recognition [SL-REC]
10.8.1: Multilingual speech recognition and identification [SL-REC-LANG]
10.8.2: Multi-talker speech recognition [SL-REC-MULT]
10.8.3: Speech modeling for speech recognition [SL-REC-SMOD]
10.8.4: Adaptation and customization for speech-to-text [SL-REC-ADAP]
11: Applied Signal Processing Systems [AS]
11.1: Integrating signal processing and computing [AS-CMP]
11.1.1: Quantum and quantum-inspired signal processing [AS-CMP-QUAN]
11.1.2: Neuromorphic computing [AS-CMP-NEUR]
11.1.3: Edge and embedded computing [AS-CMP-EDGE]
11.1.4: Energy-aware computing [AS-CMP-ENGY]
11.1.5: Hardware accelerators [AS-CMP-HRDW]
11.1.6: Resource-efficient machine learning [AS-CMP-RESC]
11.1.7: Signal processing and generative AI systems [AS-CMP-GPT]
11.1.8: Processing-in-memory signal processing systems [AS-CMP-PIM]
11.2: Signal processing application systems [AS-APP]
11.2.1: Autonomous systems [AS-APP-AUTO]
11.2.2: Internet of things [AS-APP-IOT]
11.2.3: Robotics [AS-APP-ROBO]
11.2.4: Radar, sonar and acoustic systems [AS-APP-RADR]
11.2.5: Safe and trustworthy systems [AS-APP-SAFE]
11.2.6: Applications of generative AI and foundation models [AS-APP-GPT]
11.2.7: Other emerging topics in signal processing systems [AS-APP-OTHR]
12: Signal Processing Education [ED]
12.1: Curriculum development [ED-CUR]
12.2: Resouces and tools for signal processing education [ED-RES]
12.3: Pedagogical models for signal processing education [ED-MOD]
12.4: Case studies in signal processing education [ED-APP]