جست‌وجوی استاد و پژوهشگر

PAOLO CASTELLINIمشاهده پروفایل

دانشیار

• Mechanical Measurements
• Vibration Analysis
• Hyperspectral Imaging
• +۴ مورد دیگر

Paolo Castellini is an Associate Professor at the Department of Industrial Engineering and Mathematical Sciences, Università Politecnica delle Marche (UNIVPM). His research focuses on mechanical measurements, vibration analysis, and advanced imaging techniques. He teaches courses in the College of Engineering and has published extensively on applications of hyperspectral imaging, 3D scanning, and neural networks in industrial and biomedical contexts. Office hours: Monday-Friday, 08:30-13:00 Email: p.castellini@univpm.it Location: via Brecce Bianche, 12, Ancona Recent research trends include vibration decoupling metastructures, microplastic detection in biological samples, and UAV-based photogrammetric analysis of olive trees. His publications emphasize non-destructive testing, acoustic beamforming, and uncertainty evaluation in measurement systems. Notable projects involve mmWave radar displacement analysis, cardiac simulators for prosthetic valves, and light-controlled polymer films for mechanical motion.

Tuomas Virtanenمشاهده پروفایل

استاد

• Audio signal processing
• Machine listening
• Sound source separation
• +۵ مورد دیگر

Tuomas Virtanen is a Professor at Tampere University's Signal Processing Research Centre within the Faculty of Information Technology and Communication Sciences. His primary affiliation is the Computing Sciences department, where he leads the Audio Research Group . His work focuses on computational analysis of audio signals, machine listening, and acoustic scene understanding. Research Interests: Audio signal processing, content analysis of audio, sound source separation, acoustic scene classification, speech processing (including noise-robust ASR and speaker recognition), and machine learning techniques such as deep neural networks and statistical modeling. His group develops methods for sound event detection, localization, and tracking in complex acoustic environments. Key Contributions: Leading the DCASE (Detection and Classification of Acoustic Scenes and Events) challenges, developing open-source datasets like TUT Acoustic Scenes and STARSS, and advancing techniques in audio captioning, privacy-preserving audio processing, and multimodal learning. Notable Projects: Creation of the Audio Research Group , development of the Clotho audio captioning dataset, and pioneering work in zero-shot audio classification using semantic embeddings. His research bridges signal processing fundamentals with modern machine learning paradigms.

Pardhasaradhi Bethiمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Deep Learning
• Signal Processing
• +۷ مورد دیگر

Pardhasaradhi Bethi is a Research Fellow at the Department of Information and Communication Technology , University of Agder , specializing in lightweight deep learning models for edge computing and radar signal processing. Focus areas include object classification, sound recognition, and drone surveillance Collaborates with colleagues like Linga Reddy Cenkeramaddi and Srihari Pathipati Research Interests encompass edge AI , radar-communication convergence , and real-time IoT integration . His work emphasizes optimizing neural networks for low-power devices and improving tracking frameworks. Publication Trends show a strong focus on automotive radar , drone detection , and human activity analysis using deep learning techniques.

• Signal processing
• Audio processing
• Spatial audio
• +۷ مورد دیگر

Professor Christian Ritz serves as Director of SMART and Professor in the School of Electrical, Computer and Telecommunications Engineering within the Faculty of Engineering and Information Sciences at the University of Wollongong. He has held his professorship since 2017 and served as Associate Dean (Research) in 2022 and Associate Dean (International) from 2016-2021. Professor Ritz's research focuses on signal processing for speech, audio, acoustics and visual information. His current projects include microphone array signal processing for enhancing sound recorded in classrooms, sound scene classification for dementia care environments, spatial audio for Virtual and Augmented Reality, and undersea surveillance. His work spans multiple technical domains including signal processing, audio engineering, computer vision, and communications engineering. His recent publication trends show a strong focus on neural network applications for audio processing, particularly for classroom monitoring and spatial audio reproduction. His work integrates deep learning with traditional signal processing techniques to address challenges in spatial audio synthesis, room impulse response generation, and directional audio reproduction. He has also contributed to applications in mining automation, public safety surveillance, and positioning systems. Professor Ritz is a member of the Centre for Signal and Information Processing (CSIP), part of the Signals, Information and Communications Research Institute (SICOM) at the University of Wollongong. His research is funded by the ARC, overseas research institutes, and defense organizations. His grant portfolio is extensive, including projects such as AI/IoT-powered Airborne System for Monitoring Water Level and Tidal Floods, Multi-Modal Satellite-based Vessel Surveillance, Net Zero Industry and Innovation Program, Smart Eye: Airborne and AI-Driven Assessment Solution of Sugarcane, and numerous projects related to spatial audio and underwater surveillance. He has secured funding from diverse sources including the Australian Research Council, defense organizations, and international collaborations. Professor Ritz maintains an active research laboratory environment focused on signal and information processing. His work spans multiple application domains from classroom monitoring to underwater mine detection, with strong connections to both academic and industry partners. His research group appears to focus on practical applications of signal processing techniques to solve real-world problems across various domains.

• Robotics
• Simultaneous Localization and Mapping (SLAM)
• Sensor Fusion
• +۵ مورد دیگر

Maurice Fallon is a Professor of Engineering Science at the University of Oxford and a Royal Society University Research Fellow, leading the Dynamic Robot Systems Group (Perception) at the Oxford Robotics Institute. His research focuses on robust probabilistic methods for localization and mapping in challenging environments through advanced sensor fusion. Education: Electronic Engineering, University College Dublin PhD in Acoustic Source Tracking, University of Cambridge Research Interests: Dr. Fallon specializes in probabilistic state estimation , legged robot navigation , and dynamic motion planning for autonomous systems operating in vision-denied or complex natural environments. His work emphasizes robustness through multi-sensor integration , with applications spanning disaster response, forestry, and industrial inspection. Key innovations include terrain-aware locomotion and long-term autonomy frameworks. Publication Trends: Recent work (2024-2025) demonstrates a strategic shift toward forest robotics and long-term industrial inspection , leveraging legged and aerial platforms. There is strong emphasis on vision foundation models for place recognition, scalable 3D reconstruction using neural radiance fields, and open-vocabulary scene understanding . The research consistently addresses real-world challenges like lighting variations, sensor dropout, and environmental dynamics. Scientific Awards: Royal Society University Research Fellowship 4x Best Paper Awards at ICRA Nominations at Intelligent Vehicles, AAAI, and Humanoids conferences Advising and Grants: Dr. Fallon has secured major funding as PI/Co-I for EU/UK projects including ORCA, RAIN, THING, MEMMO, and the DARPA SubT-winning CERBERUS team. Current initiatives include the Horizon Europe DigiForest project and UKAEA collaborations. He mentors PhD students and postdocs in robotics systems development, though specific advisees aren't listed in source materials. Labs and Teams: He directs the Dynamic Robot Systems Group, which achieved global recognition through DARPA Robotics Challenge participation and SubT Challenge victory. The team operates specialized facilities for legged robot testing and maintains partnerships with nuclear energy and forestry sectors for field deployment.

Slim Essidمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Signal Processing
• +۵ مورد دیگر

Slim Essid is a Full Professor at Télécom Paris and coordinator of the Audio Data Analysis and Signal Processing (ADASP) group. He holds a PhD and HDR from Université Pierre et Marie Curie (UPMC). His research focuses on machine learning, artificial intelligence, and signal processing applied to temporal data analysis, including multiview learning, representation learning, and structured prediction. Applications span music content analysis (MIR), multimodal perception (e.g., EEG data analysis), and human behavior analysis. He has advised 15 PhD students and collaborated on over 14 post-doctoral projects. Education: PhD in Signal Processing, Université Pierre et Marie Curie (2005) Habilitation (HDR), Université Pierre et Marie Curie (2015) M.Sc. in Digital Communication Systems, Télécom ParisTech (2002) Engineer Degree, École Nationale d’Ingénieurs de Tunis (2001) Research interests emphasize multimodal learning, self-supervised representation learning, and audio-visual fusion. Key projects include sound-prompted segmentation, zero-shot audio captioning, and EEG-based auditory attention decoding. Over 150 peer-reviewed publications exist across conferences like NeurIPS, ICML, and journals like IEEE Transactions. Active in reviewing for top-tier venues and advising French/EU research projects. Labs/Teams: Member of the Signal, Statistics and Learning (S2A) research team and the Information Processing and Communication Laboratory (LTCI).

• Audio Processing
• Machine Learning
• Sound Event Detection
• +۳ مورد دیگر

Paul Primus is a researcher at the Institute of Computational Perception, Johannes Kepler University Linz, specializing in audio processing and machine learning. His work focuses on sound event detection, acoustic scene classification, and language-based audio retrieval, with significant contributions to the DCASE (Detection and Classification of Acoustic Scenes and Events) challenges. Education: Dr. (PhD) MSc BSc Research Interests: Primus's research bridges audio signal processing and deep learning, addressing real-world challenges in machine listening. His work emphasizes device invariance, data efficiency, and transformer architectures for audio analysis. Key contributions include knowledge distillation for audio retrieval, multi-stage transformer training, and novel approaches to language-audio interaction. He actively explores low-complexity solutions suitable for embedded systems and edge deployment. Publication Trends: Primus's recent work (2023-2025) shows a clear trajectory toward multimodal audio-language systems, leveraging transformers and pretraining techniques. His publications increasingly focus on data efficiency, device generalization, and practical deployment constraints, as evidenced by his DCASE challenge submissions. The integration of metadata and cross-modal alignment represents a growing research emphasis. Activities: Adversarial Robustness in Data Augmentation (2020) Exploiting Parallel Audio Recordings to Enforce Device Invariance in CNN-based Acoustic Scene Classification (2019) Labs and Teams: Primus is a core member of the Institute of Computational Perception at JKU, which leads research in computational audio analysis. The institute maintains strong participation in international challenges like DCASE and collaborates extensively on audio transformer development and language-audio interaction systems.

Michael Neriمشاهده پروفایل

پژوهشگر

• Computer Vision
• Deep Learning
• Audio Processing
• +۳ مورد دیگر

Michael Neri is a Researcher in Audio Signal Processing at Tampere University and a Ph.D. student in Applied Electronics at Roma Tre University. His primary affiliation is with the Department of Signal Processing at Tampere University. He holds a B.Sc. in Information Engineering from the University of Padua (2019) and an M.Sc. in ICT for Internet & Multimedia from the University of Padova (2021). He was a visiting PhD student at Tampere University of Technology in 2023. His research focuses on Audio Processing, Computer Vision, and Deep Learning, with applications in safety and security. Key projects include RAILGAP (EU Horizon 2020), which uses GNSS for rail positioning, INSECTT (ECSEL EU Project) for secure IoT-AI systems, and ISEEYOO (University of Padova Grant) for anomaly detection in Cyber-Physical Systems. He has also contributed to the ESA-funded VOLIERA project on multi-sensor railway positioning. Neri's work spans environmental sound classification, speaker localization, and AI-driven multimedia quality assessments. His research is supported by grants from the European Union and ESA programs. He actively collaborates on projects addressing occupational safety through VR integration and anomaly detection in audio signals. Contact him via michael.neri@tuni.fi or his professional profiles on ResearchGate and GitLab .

Philip Jacksonمشاهده پروفایل

استاد

• Machine Audition
• Audio-Visual Machine Learning
• Spatial Audio
• +۹ مورد دیگر

Philip Jackson is a Professor in Machine Audition at the University of Surrey's Centre for Vision, Speech and Signal Processing (CVSSP) within the School of Computer Science and Electronic Engineering. His research focuses on audio-visual machine learning, spatial audio technologies, object-based media production, and responsible AI applications. He has over 200 publications and an h-index of 30, contributing to projects like Nephthys, BALTHASAR, and SAVEE. He served as an associate editor for Computer Speech and Language and reviewer for major journals like IEEE/ACM Transactions on Audio, Speech and Language Processing. Key research interests include sound localization, audio-visual tracking, reverberation modeling, and ethical implications of AI in media. His work spans datasets such as Tragic Talkers, SurrRoom, and ForecasterFlexOBM, advancing immersive audio-visual systems and personalized media experiences. Notable projects include spatial audio reproduction for VR, parametric room acoustics, and citizen council research on algorithmic transparency. He has advised over 20 PhD/Master’s students and collaborates with industry on applications like the Catalan Pavilion's Venice Biennale installation. His grants include UK postdoctoral fellowships and leadership in EU-funded initiatives like UDRC2 and QESTRAL.

• Artificial Intelligence
• Machine Learning
• Artificial Neural Networks
• +۱۹ مورد دیگر

Professor Stephan Chalup is a leading academic in Artificial Intelligence and Machine Learning at the University of Newcastle , affiliated with the School of Information and Physical Sciences and the Data Science and Statistics department. He leads the Interdisciplinary Machine Learning Research Group (IMLRG) and the Newcastle Robotics Lab , where his team has achieved global recognition, including two RoboCup world championships. PhD in Computing Science , Queensland University of Technology (2002) Diplom in Mathematics with Neuroscience , University of Heidelberg His research focuses on artificial neural networks , deep learning , and high-dimensional data analysis , with applications in robotics, computer vision, medical imaging, and architectural analysis. He investigates how biological neural systems inspire robust AI models, particularly in topological data analysis and 4D vision . The recent publications highlight a strong trend in topological and geometric machine learning , with a focus on 4D data analysis , multi-agent reinforcement learning , and robot perception . His work bridges theoretical AI with practical industry solutions in transport, healthcare, and robotics. RoboCup World Champion (2008, 2006) Leadership Excellence, CESE (2024) Supervision Research Excellence Award (2015) Multiple Best Student Paper Awards (2019, 2018, 2011) Chalup has supervised numerous students and led significant research projects, including the ARC Discovery Project on estimating topology of low-dimensional data. His lab fosters interdisciplinary collaboration and innovation, with alumni working in top global tech roles. He is an active keynote speaker and program committee member in major AI conferences. His labs, including the Newcastle Robotics Lab , are equipped with state-of-the-art robots and computing systems, supporting cutting-edge research in humanoid robotics, autonomous navigation, and AI-driven data analysis.

Alexandre Mendesمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Optimization
• Deep Learning
• +۶ مورد دیگر

Dr. Alexandre Mendes is a Senior Lecturer at the University of Newcastle, affiliated with the School of Information and Physical Sciences and the Department of Computing and Information Technology. His research focuses on machine learning, optimization, and deep learning, with applications in robotics, energy systems, and transport logistics. Education: PhD in Electrical Engineering, Universidade Estadual de Campinas, Brazil MSc in Electrical Engineering, Universidade Estadual de Campinas, Brazil Bachelor in Applied Mathematics, Universidade Estadual de Campinas, Brazil Graduate Certificate in the Practice of Tertiary Teaching, University of Newcastle Research Interests: Mendes specializes in applying machine learning to real-world problems such as optimizing railway logistics, predicting solar energy output, and improving robotic systems. His work bridges theory and practice, collaborating with industries like CSIRO and Hunter Valley Coal Chain. Grants and Funding: He has secured over AU$1.5M in grants from ARC, CSIRO, and industry partners. Key projects include coal train scheduling optimization and solar energy prediction using deep learning. Awards: Work Integrated Learning Staff Member of the Year (2020) Best Student Paper Award (2012) Pro Vice-Chancellor's Research Excellence Award (2011) Labs and Teams: He leads the NUbots robotics team, focusing on humanoid robots and autonomous systems. His research group collaborates on projects like the Sydney Metro and Hunter Region rail network optimization.

• Audio Signal Processing
• Beamforming
• Speech Enhancement
• +۴ مورد دیگر

Kouei Yamaoka is an Assistant Professor in the Department of Electrical Engineering and Computer Science at Tokyo Metropolitan University's College of Engineering, specializing in advanced audio signal processing techniques. With over 24 publications from 2017-2024, Yamaoka has established a strong research presence in the international signal processing community. Yamaoka's research focuses on innovative beamforming techniques, time delay estimation methods, and speech enhancement algorithms. Their work consistently addresses challenging problems in multichannel audio processing, particularly in underdetermined scenarios where the number of sources exceeds available microphones. Key contributions include causal distortionless response beamforming, minimum-spanning-tree-based time delay estimation robust to outliers, and sound field interpolation for rotation-invariant processing. Analysis of Yamaoka's publication trends reveals a consistent focus on practical audio processing solutions with applications in source separation, speech enhancement, and acoustic scene analysis. The research demonstrates strong theoretical foundations combined with practical implementation considerations, particularly for real-time and online processing scenarios. Recent work has expanded into deep learning applications for bioacoustic analysis, as evidenced by the 2023 publication on marmoset vocalization analysis. Yamaoka maintains a productive collaboration network, with frequent co-authorship with Nobutaka Ono (20 joint publications), Shoji Makino (9 joint publications), and other researchers in the Japanese signal processing community. Publications appear consistently in top venues including IEEE/ACM Transactions on Audio Speech and Language Processing, APSIPA, EUSIPCO, and ICASSP.

• Signal Processing
• Machine Learning
• Optimization
• +۷ مورد دیگر

Hiroyuki Kasai is a Full Professor at the School of Fundamental Science and Engineering, Waseda University, where he leads research in signal processing, machine learning, and optimization. He holds a B.Eng. (1996), M.Eng. (1998), and Dr.Eng. (2000) in Electronics, Information, and Communication Engineering from Waseda University. His career includes positions as Associate Professor and Professor at the University of Electro-Communications (2007-2019), Senior Policy Researcher at Japan's Cabinet Office (2011-2013), and visiting roles at Technical University of Munich and British Telecom. His research spans: Fundamental methodologies : Riemannian optimization, stochastic gradient algorithms, tensor decomposition Applied domains : Network analysis, multimedia systems, environmental sound processing, video coding Emerging areas : Low-rank modeling, manifold learning, and large-scale anomaly detection His publications focus on efficient algorithms for high-dimensional data, with recent work emphasizing Riemannian manifold optimization and real-time tensor analysis. This includes development of open-source tools like SGDLibrary (MATLAB) and McTorch (PyTorch) for optimization tasks. Awards include: IEEE ICCE Best Paper Award (2011) Yamashita Memorial Award (2003) Ericsson Young Scientist Award (2001) 電気通信普及財団賞 (2015) IEICE Service Recognition Award (2010) He maintains memberships in IEEE, IEICE, IPSJ, and JSIAM, and has contributed to over 100 peer-reviewed publications with significant citation impact (h-index 27 via Google Scholar).

Emirhan GÜLERمشاهده پروفایل

استادیار

• Turkish Folk Music
• Musicology
• Bağlama (Turkish String Instrument)
• +۵ مورد دیگر

Emirhan GÜLER serves as an Assistant Professor in the Department of Musicology at Adıyaman University State Conservatory. He began his academic career in 2015 as a Research Assistant at the same institution and was promoted to Assistant Professor on February 2, 2023. His academic journey includes a Doctorate in Music Education from Gazi University (2018-2021), a Master's in Musicology from Atatürk University (2013-2017), and a Bachelor's degree in Musicology from Atatürk University (2009-2013). Doctorate: Gazi University, Institute of Educational Sciences, Music Education (2018-2021) Master's: Atatürk University, Institute of Fine Arts, Musicology (2013-2017) Bachelor's: Atatürk University, Faculty of Fine Arts, Department of Musicology (2009-2013) GÜLER's research primarily focuses on Turkish folk music traditions, particularly in the Adıyaman region, with special attention to bağlama (a traditional Turkish string instrument), music theory, and pedagogy. His work bridges theoretical musicology with practical performance considerations, examining how traditional music functions within cultural contexts while addressing contemporary educational applications. He has published extensively on regional music characteristics, instrumentology, and the relationship between folk literature and musical expression. His recent publications reveal a strong regional focus on Adıyaman's musical heritage while expanding to broader Turkish folk music traditions. GÜLER frequently examines the intersection of music with social practices, agricultural life, and religious ceremonies. His technical work on bağlama construction, performance techniques, and tuning systems demonstrates deep engagement with both theoretical frameworks and practical musicianship. The research shows consistent attention to preserving traditional knowledge while adapting it for contemporary educational contexts. GÜLER has been involved in significant projects including the establishment of a Music Research and Sound Recording Studio Infrastructure at Adıyaman University State Conservatory (2019-2021) and research on Adıyaman Folk Songs (2015). He teaches courses in Turkish Folk Theory and Repertoire, Individual Instrument (Bağlama), Introduction to Musicology, and various music history and analysis courses across multiple academic years.

• Signal Processing
• Machine Learning
• Acoustic Communication Systems
• +۴ مورد دیگر

Rainer Martin is a Professor of Information Technology and Communication Acoustics at Ruhr-Universität Bochum, where he has been affiliated since 2003. He served as Dean of the Faculty of Electrical Engineering and Information Sciences from 2007 to 2009. He holds a Dipl.-Ing. and Dr.-Ing. from RWTH Aachen University and an M.S.E.E. from Georgia Institute of Technology. His research focuses on signal processing, machine learning, and acoustic systems for voice communication, hearing instruments, and human-machine interfaces. Key areas include speech enhancement, noise reduction, binaural processing, cochlear implant optimization, and acoustic sensor networks. His work integrates deep learning with traditional signal processing for real-world applications. Recent publications emphasize neural networks for noise control, hearing aid algorithms, radar interference mitigation, and auditory perception modeling. Trends show strong convergence of machine learning with acoustic engineering, particularly in low-power embedded systems and healthcare applications. Awards: Fellow of the IEEE He leads projects on acoustic sensor networks, automotive radar, and hearing technology, collaborating with industry partners. His lab develops algorithms for hearing aids, cochlear implants, and robust communication systems, with patents spanning noise reduction, echo control, and speech enhancement.