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Konrad KowalczykView profile

Associate Professor

• Speech and Audio Signal Processing
• Machine Learning
• Deep Neural Networks
• +6 more

Konrad Kowalczyk is an Associate Professor at AGH University of Science and Technology in Krakow, Poland, where he heads the Signal Processing Group within the Faculty of Computer Science, Electronics and Telecommunications. With extensive international experience from institutions including Queen's University Belfast, Stanford University, and Fraunhofer Institute, he has established himself as a leading researcher in audio and speech signal processing. His academic journey includes B.Eng. and M.Sc. degrees from AGH University (2005), a Ph.D. from Queen's University Belfast (2009), and a Habilitation in ICT from AGH University (2020). B.Eng. and M.Sc. in Electronics and Telecommunications, AGH University of Krakow (2005) Ph.D. in Electronics, Queen's University Belfast, UK (2009) Habilitation (D.Sc.) in Information and Communication Technology, AGH University of Krakow (2020) Kowalczyk's research spans multiple cutting-edge areas in audio processing, with particular focus on speech and audio signal processing enhanced by machine learning techniques. His work integrates deep neural networks with traditional signal processing methods to address challenges in array signal processing , speech enhancement , and speaker recognition . The research group he leads explores innovative applications in distributed signal processing for IoT , acoustic event detection , and spatial audio rendering , bridging theoretical advances with practical implementations. His recent publications demonstrate a clear trend toward integrating deep learning with traditional signal processing techniques, particularly in speaker diarization, source separation, and robust speech recognition. The research increasingly focuses on real-world applications requiring reverberation-robust processing , distributed microphone array systems , and end-to-end neural architectures that can operate in challenging acoustic environments. There's a noticeable shift toward more complex, integrated systems that combine multiple signal processing tasks. Stanislaw Staszic Medal for best graduate of AGH (2005) IEEE Best Student Paper Contest finalist (2007) AES Student Technical Paper Award winner (2008) Best Student Paper Award at IWAENC conference (2014) Best Paper Awards at IEEE SPA conferences (2016, 2019) Polish Ministry of Science Scholarship for Distinguished Young Scientists (2016-2019) Prime Minister Award for outstanding scientific achievements (2020) As Principal Investigator, Kowalczyk leads multiple significant research projects including "Acoustic Intelligence" (2024-2028) funded by National Science Center, and "Deep extraction for robust speech recognition" (2023-2028). He has successfully secured funding from prestigious programs including First TEAM from the Foundation for Polish Science, and EU FP7 projects. His research group actively supervises Ph.D., M.Sc., and B.Eng. students, with strong connections to international institutions including Aalto University and IEEE Signal Processing Society. The research output includes numerous journal publications, conference papers, patents, and software implementations that have advanced the field of audio signal processing. Kowalczyk leads the Signal Processing Group at AGH University, which focuses on developing innovative solutions for speech and audio processing challenges. The group maintains strong collaborations with international institutions including Aalto University (Finland), and participates in European research initiatives. Their work spans theoretical development through practical implementation, with applications ranging from medical voice assistants to distributed acoustic sensor networks.

Slim EssidView profile

Professor

• Machine Learning
• Artificial Intelligence
• Signal Processing
• +7 more

Slim Essid is a Full Professor at Télécom Paris, leading the Audio Data Analysis and Signal Processing (ADASP) group. He holds a Doctorat (Ph.D.) and Habilitation from Université Pierre et Marie Curie (UPMC). With 15+ years of research experience, he has advised 15 PhD graduates and currently co-advises 10 others. His work focuses on machine learning, signal processing, and multimodal systems, publishing over 150 peer-reviewed papers. He serves as a reviewer for top journals/conferences (e.g., IEEE Transactions) and research funding agencies. Education: State Engineering Degree, École Nationale d’Ingénieurs de Tunis (2001) M.Sc. (D.E.A.) in Digital Communication Systems, École Nationale Supérieure des Télécommunications, Paris (2002) Ph.D., Université Pierre et Marie Curie (2005) Habilitation (HDR), UPMC (2015) Research Interests: Multimodal learning, self-supervised representations, audio-visual segmentation, music structure analysis, domain generalization, and speech enhancement. Recent publications highlight innovations like TACO (training-free sound-prompted segmentation) and CLOUDS (domain-generalized semantic segmentation framework using foundation models). His work bridges audio processing with vision and language models, emphasizing unsupervised/zero-shot approaches. Key achievements include state-of-the-art methods in sound event detection, speaker diarization, and music segmentation. He collaborates with 14 post-docs and leads projects funded by French/EU agencies.

Ronald N. MilesView profile

Professor

• Mechanics
• Acoustics
• MEMS (Microelectromechanical Systems)
• +6 more

Ronald N. Miles is a Distinguished Professor in the Department of Mechanical Engineering at Binghamton University, part of the Watson School of Engineering and Applied Science. He has held various administrative roles including Director of Graduate Studies, Department Chair, and Associate Dean for Research. His expertise spans mechanics, acoustics, MEMS, neurobiology, and control systems, with a focus on bio-inspired microacoustic sensors for healthcare and consumer electronics. Educated at the University of California, Berkeley (BSEE) and the University of Washington (MS/PhD in Mechanical Engineering), Miles has over 40 years of academic and industry experience. His research has led to over 100 publications, 20 patents, and significant grants totaling $17 million. Notable achievements include the Chancellor's Award for Excellence in Teaching and the Research Foundation's Outstanding Inventor Award. Miles' research emphasizes bio-inspired sensor design, acoustic flow sensing, and MEMS technology. His team has developed innovative microphones mimicking insect hearing mechanisms, with applications in hearing aids and medical devices. Current projects include NIH-funded work on acoustic measurements in the human ear canal. Award highlights include recognition for teaching (1996-1997 Chancellor's Award) and research innovation, including the 2005 First Patent Award. His work bridges engineering and biology, with labs focused on acoustic core technologies and vibrations research. Miles also serves as Associate Editor for the ASME Journal of Vibration and Acoustics.

Bruce WigginsView profile

Associate Professor

• Audio Engineering
• 3D Audio
• Ambisonics
• +5 more

Bruce Wiggins is an Associate Professor in Audio Engineering at the College of Science and Engineering. His research focuses on spatial audio technologies, including Ambisonics, binaural auralization, and 3D audio systems. Notable projects include the GASP guitar system and WHAM webcam-based head-tracked audio solutions. He has contributed to advancements in microphone array calibration, speaker array modeling, and virtual reality audio integration. His work bridges theory with practical applications in music technology and acoustic engineering. Education: PhD in Audio Engineering (2004). Research Interests: Ambisonics, spatial audio capture and reproduction, 3D audio for virtual reality, binaural rendering, and innovative musical instrument design. His work emphasizes practical implementations such as the GASP guitar system and calibration tools for low-cost microphone arrays. Article Trends: Recent publications address virtual stereo microphone techniques (2024), dynamic electrical systems (2024), and browser-based 3D audio (2023). Earlier work explores head-tracking algorithms (2016–2020) and acoustic modeling for domestic environments (2017). Grants/Advising: No explicit grants listed. Advising details unavailable but has collaborated with numerous researchers on projects like WHAM and GASP. Labs/Teams: Active in interdisciplinary teams developing spatial audio tools and instruments, including collaborations on virtual reality auralization and ambisonic guitar systems.

• Acoustic Signal Processing
• Blind Source Separation
• Digital Signal Processing
• +6 more

Shoji Makino is a Professor at Waseda University's Graduate School of Information, Production and Systems. He has held academic and research positions at institutions such as the University of Tsukuba and NTT Communication Science Laboratories. His work spans acoustic signal processing, blind source separation, and adaptive filtering. Education: Ph.D., Tohoku University (1993.03) Mechanical Engineering, Tohoku University Graduate School of Engineering (1979.04–1981.03) Engineering, Tohoku University Faculty of Engineering (1975.04–1979.03) Research Interests: His research focuses on acoustic signal processing for speech and audio, including blind source separation (BSS) , beamforming , and adaptive filtering . He pioneered methods for solving permutation alignment in frequency-domain BSS and developed geometrically constrained ICA techniques. Scientific Awards: Hoko Award (2018.10, Hattori Hokokai Foundation) Outstanding Contribution Award of the Institute of Electronics, Information, and Communication Engineers (2018.06) IEEE Signal Processing Society Best Paper Award (2014.01) IEEE Fellow (2004.01) IEICE Achievement Award (1997.05) Committee Memberships: He has served as Chair of the IEEE CAS Society's Blind Signal Processing TC, General Chair of IEEE WASPAA2007, and Associate Editor of IEEE Trans. SAP. He is actively involved in EURASIP, APSIPA, and the Acoustical Society of Japan.

• Structural Dynamics
• Vibrations
• Acoustics
• +8 more

Jonathan Blotter is a Professor in the Department of Mechanical Engineering at the College of Engineering, Brigham Young University (BYU). His research and teaching focus on structural dynamics, vibrations, acoustics, and experimental mechanics, with extensive use of laser-based measurement techniques. Education: Doctor of Philosophy, Mechanical Engineering, Virginia Polytechnic Institute and State University, 1996 Master of Science, Mechanical Engineering, Utah State University, 1993 Bachelor of Science, Mechanical Engineering, Utah State University, 1991 His research interests include structural dynamics, vibrations, acoustics, noise and vibration control, solid mechanics, electro-optic holography, and scanning laser Doppler vibrometry. His work bridges experimental techniques with computational modeling to analyze structural energy flow and dynamic behavior. He has made significant contributions to the understanding of power flow in plates and beams, nearfield acoustic holography, and flow-induced vibrations. The most recent articles reflect a consistent focus on experimental acoustics and vibration measurement. Key themes include noise control using microphone arrays, sound quality assessment in consumer products, optical interferometry for deformation measurement, and fluid-structure interaction in piping systems. These works span disciplines of mechanical engineering, acoustics, optics, and thermal sciences, with subfields such as vibroacoustics, metrology, and non-destructive testing. Scientific Awards: No scientific awards mentioned in the provided text. Advising and Grants: Dr. Blotter has advised numerous graduate students, including 14 Master’s students and one Ph.D. student. His advising spans experimental and computational projects in vibrations, acoustics, and mechanical testing. While specific grant details are not listed, his publication record suggests sustained research activity supported by academic and possibly industrial or federal funding, particularly in experimental mechanics and acoustics. Labs and Research Teams: Though not explicitly named, Dr. Blotter's work implies leadership in an experimental mechanics and acoustics laboratory at BYU. His collaborations with students and co-authors like Sommerfeldt, West, and Kimber suggest active research teams focused on laser-based measurement, noise control, and structural dynamics.

Julian TachellaView profile

Researcher

• Signal Processing
• Machine Learning
• Computational Imaging
• +5 more

Dr. Julian Tachella is a CNRS Research Scientist at the Sisyph Laboratory of École Normale Supérieure de Lyon, with co-founder/CSO roles at Blur Labs. His career spans signal processing, machine learning, and computational imaging, focusing on inverse problems and self-supervised learning. Affiliation: CNRS (French National Centre for Scientific Research), Sisyph Laboratory, École Normale Supérieure de Lyon Co-founder & CSO: Blur Labs (AI/Imaging startup) Research Interests: At the intersection of signal processing and deep learning , his work addresses imaging inverse problems through self-supervised methodologies (e.g., UNSURE, Generalized R2R) that eliminate ground-truth requirements. Key contributions include equivariant imaging frameworks for stability, spline sketches for photon-counting lidar compression, and uncertainty quantification techniques with equivariant bootstrapping. Recent Trends: 2025 publications emphasize lightweight architectures for multi-domain reconstruction (CT, super-resolution) and noise-agnostic SURE methods. 2024 works focus on audio declipping , compressed lidar , and nonlinear algorithm unrolling with applications in autonomous vehicles and medical imaging. Scientific Awards: Best Student Paper Award at ICASSP’22 Collaborations & Leadership: He leads the DeepInverse open-source project and develops algorithms for real-time 3D lidar reconstruction. His team includes researchers from University of Edinburgh and Grenoble INP, with applications in automotive lidar and underwater imaging.

José PICHERALView profile

Professor

• Signal Processing
• Acoustic Imaging
• Array Processing
• +5 more

José Picheral is a Professor at CentraleSupélec, affiliated with the Laboratory of Signals and Systems (L2S). He holds a PhD (2003) and HDR (2017) in high-resolution signal processing methods and inverse problems. His research focuses on array processing, source localization, acoustic imaging, and vibration analysis, with applications in aeroacoustics, automotive systems, and industrial monitoring. He has supervised multiple PhD students and contributed to projects like Valeo’s smartphone-based car key replacement system. Education: Engineering Degree: Supélec (1999) and Politecnico di Milano (1999, Erasmus-TIME) PhD: Paris Sud University (2003) Habilitation (HDR): Université Paris Sud (2017) Research Interests: High-resolution methods for distributed sources, sparse signal processing, acoustic imaging, asynchronous measurements, and sensor array design. Current projects include spatial source covariance estimation, EEG spectrum analysis, and automotive applications using smartphone localization. Key Contributions: Over 50 publications in top journals/conferences (e.g., IEEE Transactions, ICASSP). Notable work on MUSIC algorithm robustness, DAMAS optimization, and sparse approaches for tip-timing signals. Advising & Collaboration: Supervised 7 PhD students. Collaborations with SAFRAN, Valeo, and academic teams in Bayesian inference and inverse problems. Active in L2S’s Inverse Problems Group and SYCOMORE team. Labs/Teams: Member of L2S’s Signal Processing and Statistics group, leading research in systems and control, telecommunications, and energy systems.

Jonas BraaschView profile

Professor

• Acoustic Virtual Reality
• Binaural Hearing
• Auditory Modeling
• +3 more

Jonas Braasch is a Professor at Rensselaer Polytechnic Institute's School of Architecture and Associate Director for Research at the Experimental Media and Performing Arts Center (EMPAC). He teaches in the Graduate Program in Architectural Acoustics and leads research on collaborative virtual reality, binaural hearing, auditory modeling, and sensory substitution devices. His work has been funded by NSF, NSERC, and European agencies, and he holds dual PhDs in Electrical Engineering/Information Science and Musicology. His research integrates acoustic virtual reality systems, immersive museum soundscapes, telematic music, and assistive instrument design. Recent publications focus on networked immersive environments, spatial audio techniques, museum acoustics classification, and innovations in wind instrument design for accessibility. Publications emphasize the application of deep learning to acoustic prediction and multisensory integration.

Stephan PreihsView profile

Researcher

• Acoustics
• Digital Signal Processing
• Audio Coding
• +6 more

Stephan Preihs is a postdoctoral researcher and group leader at the Institute of Communications Technology of the Leibniz University Hannover , with a focus on acoustics, digital signal processing, and immersive audio systems. He received his Dipl.-Ing. in electrical engineering (communications engineering) from the same university in 2010 and his Dr.-Ing. in 2016. Education: Dipl.-Ing., Electrical Engineering (Communications Engineering), Leibniz University Hannover (2010) Dr.-Ing., Leibniz University Hannover (2016) Research Interests: Acoustics for immersive audio reproduction Digital signal processing and audio coding Signal detection/classification Psychoacoustic models Audio transmission for PMSE Recent Article Trends: Deep learning in sound source localization Wind turbine noise analysis via immersive audio Advancements in headphone technology Immersion prediction in spatial audio Low-latency communication protocols Scientific Awards: Best Paper Award at IEEE International Workshop on Networked Immersive Audio (2024) AES Show 2024 Best Technical Paper Award AES Spring 2021 Student Paper Award AES Poster Award 2019 AES Convention Student Paper Award 2019 Teaching: Lecturer for '3D Audio - Fundamentals of Spatial Reproduction Systems' Lecturer for 'Applications of Digital Audio Signal Processing' Coordinator of student laboratories in 'Audio Communication and Acoustics' and 'Transmission Technology'

Stefan Albert WirlerView profile

Researcher

• Spatial Audio
• Psychoacoustics
• Speech Enhancement
• +3 more

Stefan Albert Wirler serves as a Researcher in the Department of Information and Communications Engineering at Aalto University, actively contributing to the Communication Acoustics research group specializing in Spatial Sound and Psychoacoustics. His work bridges theoretical audio signal processing with practical applications in spatial audio systems. Primary research areas include Spatial Audio, Psychoacoustics, and Speech Enhancement, with specific focus on microphone array processing, spatial post-filtering techniques, and machine learning applications for auralization. His investigations address critical challenges in virtual reality audio reproduction and headphone acoustics through innovative signal processing approaches. Analysis of his 2020-2024 publications reveals consistent advancements in spatial post-filter design, particularly through non-linear combinations and coherence-based methods for speech enhancement. His work demonstrates growing integration of machine learning with traditional signal processing, notably in rigid sphere scattering modeling and DIY headphone modifications for improved acoustic transparency. Wirler operates within Aalto University's Communication Acoustics research ecosystem, collaborating extensively with senior researchers like Ville Pulkki on projects spanning spatial audio rendering, psychoacoustic validation, and real-time audio processing systems.

• Audio Signal Processing
• Acoustic Signal Processing
• Hearing Assistive Technologies
• +3 more

Ryan Corey (he/him/his) is an Assistant Professor at the University of Illinois Chicago in the Department of Electrical and Computer Engineering, College of Engineering. His research focuses on audio and acoustic signal processing for human listening technologies such as hearing aids and augmented reality systems. Education: MS in Electrical and Computer Engineering from University of Illinois at Urbana-Champaign (2014), BSE in Electrical Engineering from Princeton University (2012) Teaching: Co-instructor for TE 401: Develop Breakthrough Projects, ranked as excellent by students multiple times Corey's research explores signal processing algorithms that combine audio signals from distributed microphone arrays to isolate desired sounds from background noise. His work has potential applications in hearing devices that can enhance specific speaker voices in crowded environments. He also investigates innovative prototyping approaches using unconventional microphone placements on wearable devices. His recent publications focus on advanced beamforming techniques, adaptive filtering methods, and sensor network applications in audio processing. These include topics such as random projections in beamforming, neural adaptive filters, and sound field interpolation using acoustic sensor networks. Scientific Honors: Best Student Paper Award at WASPAA 2019 Intelligence Community Postdoctoral Fellowship (2019) Microsoft Research Dissertation Grant (2018) National Science Foundation Graduate Research Fellowship (2013) Corey leads the Listening Technology Lab and collaborates with Professor Andrew Singer in the Augmented Listening Laboratory. He has mentored students in developing innovative listening technologies, including prototypes for hearing aids with improved directional audio capabilities and devices that modify sound environments for better hearing experiences.

• Digital Signal Processing
• Speech Communication
• Acoustic Signal Processing
• +3 more

Peter Vary is a Professor at the Faculty of Electrical Engineering and Information Technology of RWTH Aachen University, serving as Director of the Institute for Communication Systems. His work focuses on speech and audio signal processing for communication systems. Digital Signal Processing Speech Enhancement Acoustic Echo Control Microphone Array Beamforming Communication Systems Audio Compression His recent publications (2023–2024) emphasize speech coding, noise reduction, and bandwidth extension for hearing aids and mobile devices, with technical innovations in Kalman filters, hybrid digital-analog transmission, and wind noise detection. He holds a leadership role in the Institute for Communication Systems and serves as Ombudsperson for teaching in his faculty. Contact: vary@iks.rwth-aachen.de

Tuomas VirtanenView profile

Professor

• Audio signal processing
• Machine listening
• Sound source separation
• +5 more

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.

• Audio Engineering
• Room Acoustics
• Binaural Sound Reproduction
• +7 more

André Siegel is a Lecturer for special tasks at the Electronic Media Technology Group, Department of Electrical Engineering and Information Technology, Technical University of Ilmenau. He is actively involved in research and teaching related to audio engineering and acoustics, with a focus on spatial sound reproduction and simulation. His research interests span Audio Engineering , Room Acoustics , Binaural Sound Reproduction , Spatial Audio , Acoustic Simulation , and Audio Signal Processing . His work emphasizes practical implementations in real environments, including crosstalk cancellation, head-related transfer function measurement, and sound field analysis. The publications reflect a consistent research trajectory from 2005 to 2013, with a concentration on spatial audio technologies, room simulation methods, and perceptual aspects of sound reproduction. Key themes include the optimization of stereo and binaural systems, low-frequency acoustic behavior, and advanced measurement techniques using vector sensors and spherical arrays. André Siegel has not been awarded any scientific prizes or fellowships mentioned in the available data. He collaborates with researchers such as Hans-Peter Schade, Stephan Werner, and Julius T. Fricke. His work contributes to both academic knowledge and practical applications in room acoustical consultancy and immersive audio systems. He is based in Helmholtz Building, Room H 3529, and can be contacted at andre.siegel@tu-ilmenau.de.