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Robert RoweView profile

Professor

• Music Technology
• Interactive Music Systems
• Algorithmic Composition
• +3 more

Robert Rowe is a Professor of Music Technology at New York University's Steinhardt School of Culture, Education, and Human Development, and an Affiliated Faculty member at NYU Abu Dhabi. He is a core member of the Music & Audio Research Laboratory (MARL), specializing in interactive music systems, algorithmic composition, and music cognition. His work bridges music technology, computer science, and artistic performance. Bachelor of Music in Music History & Theory (University of Wisconsin, 1976) Master of Arts in Composition (University of Iowa, 1978) Ph.D. in Music and Cognition (MIT Media Lab, 1991) Rowe's research focuses on interactive systems, real-time music processing, and the intersection of music with cognitive science and artificial intelligence. His compositions have been performed globally and are featured on multiple record labels. Key contributions include the books Interactive Music Systems (1993) and Machine Musicianship (2001), which explore computational approaches to music creation and performance. His recent work emphasizes distributed performance systems, emotion recognition in music, and the application of machine learning to interactive composition. Notable projects include Melting the Darkness (2014) and Arcturus (2006), which showcase real-time interaction between performers and computational systems. Awards : First Prize in the Bourges International Electroacoustic Music Competition (1990) Grants : Multiple grants from NYU and external institutions for research in music technology and cognition. Rowe has led initiatives in NYU’s Steinhardt School, including serving as Associate Dean of Research & Doctoral Affairs, and collaborates internationally with institutions like IRCAM (Paris) and the Sweelinck Conservatory (Amsterdam). His lab, MARL, develops cutting-edge tools for music analysis, generation, and performance.

Simon LeglaiveView profile

Assistant Professor

• Audio Signal Processing
• Machine Learning
• Speech Enhancement
• +7 more

Simon Leglaive is a tenured Assistant Professor at CentraleSupélec in Rennes, France, and a researcher in the AIMAC team at the IETR laboratory (CNRS UMR 6164). He holds a PhD in Audio Signal Processing from Télécom Paris (2017), and previously served as a postdoctoral researcher at Inria Grenoble Rhône-Alpes. His academic affiliations include CentraleSupélec, Télécom Paris, and CNRS, with research collaborations across institutions including Inria, GIPSA-lab, and Sorbonne University. His research focuses on the intersection of signal processing and machine learning, particularly for audio and speech applications. Key areas include probabilistic generative models, deep learning, low-dimensional representation learning, and weakly or semi-supervised learning. His work enables advanced multimodal machine perception tasks such as speech enhancement, audiovisual speech analysis, and human mesh recovery. He teaches machine learning, deep learning, and audio signal processing at CentraleSupélec. The 15 most recent publications highlight a strong trend in deep generative modeling, especially using variational autoencoders (VAEs) and masked autoencoders, for tasks like speech enhancement, source separation, emotion recognition, and human pose recovery. There is a clear emphasis on disentangled and interpretable latent representations, multimodal fusion, and unsupervised or weakly supervised methods. Applications span from speech technology to computer vision, reflecting a cross-disciplinary approach centered on fundamental representation learning. Coordinator and Principal Investigator, DEGREASE project (ANR-23-CE23-0009) Partner, DEESSE project (ANR-24-CE23-2229) Elected member, IEEE AASP Technical Committee (2023–2025) Organizing committee, CHiME-7 UDASE challenge (2023) Conference area chair, ICASSP (2024, 2025), WASPAA (2023, 2025) Simon Leglaive advises multiple PhD students and has served on the IETR laboratory council since 2022. His academic service includes extensive reviewing for top journals and conferences in signal processing and machine learning. His research is supported by competitive grants from the French National Research Agency (ANR). He is actively involved in collaborative research through the AIMAC team at IETR and partnerships with Inria and industry labs.

Nicola DibbenView profile

Professor

• Music Psychology
• Popular Music Studies
• Digital Media and Music
• +3 more

Nicola Dibben is a Professor of Music and Faculty Director of Research and Innovation at the School of Languages, Arts and Societies, University of Sheffield. Her work bridges music cognition, popular music studies, and music digitalisation, with a focus on AI music generation, environmentalism, and social justice. Her research explores how music engagement shapes identity, emotions, and environmental values, including collaborations with Björk on the Biophilia album-app and projects like MIMA (Machine Intelligence for Music and Audio). She investigates ethical AI tools for musicians and the psychological impacts of synthetic media. Key Research Areas: Music and AI (assistive technologies, voice conversion) Environmentalism in music (Iceland, Colombia collaborations) Embodied cognition (facial surgery effects, prosociality studies) Digital transformations (VR, tablet-based music) Scientific Awards: Honorary Doctorate, University of Oslo (2022) Fellow of the Higher Education Academy (FHEA) She supervises doctoral candidates in music cognition, transgender singer experiences, and music's role in environmentalism. Her work includes Datasounds (AHRC-funded) and MiMA (Machine Intelligence for Music and Audio).

• Signal Processing
• Acoustic Sensor Networks
• Machine Learning
• +3 more

Roberto Gil Pita is a Professor at the University of Alcalá (Spain), affiliated with the Department of Signal Theory and Communications. He leads the AES3 research group focusing on acoustic and electromagnetic smart sensor networks and signal processing applications. His doctoral work (2006) centered on radar target classification using statistical and AI methods under the supervision of Dr. Manuel Rosa Zurera. His research spans signal processing, machine learning, and their applications in aerospace, biomedical systems, and smart cities. Key areas include UAV detection, emotion recognition from speech, and acoustic localization using microphone arrays. His academic background includes a doctorate from the University of Alcalá and extensive contributions to wireless acoustic sensor networks, hearing aid signal processing, and bioimpedance spectroscopy. He has developed energy-efficient algorithms for real-time audio analysis, acoustic violence detection systems, and robust methods for speech enhancement in noisy environments. His work bridges theoretical signal processing with practical engineering solutions for defense, healthcare, and urban monitoring. Research interests extend to aeroelastic flutter analysis in aviation, wearable biomedical sensors for stress assessment, and data-driven approaches for sound environment classification. He has pioneered the use of deep learning in flutter testing and acoustic event classification, contributing to datasets like REALISED for benchmarking machine learning models. Notable projects include acoustic localization of drones using microphone arrays, real-time emotion detection systems, and collaborative research in smart healthcare technologies. His work emphasizes computational efficiency and energy conservation, particularly for embedded systems and battery-operated devices.

• Acoustic Signal Processing
• Speech Enhancement
• Microphone Array Processing
• +6 more

Dr Alastair Moore is a Researcher in the Dyson School of Design Engineering at Imperial College London, affiliated with the Speech and Audio Processing Laboratory. He holds a PhD from Imperial College and has industrial experience in consumer electronics. His research focuses on microphone array signal processing, speech enhancement in reverberant environments, and applications in augmented reality, hearing aids, and robot audition. Education: MEng in Electronic Engineering with Music Technology Systems (1st class) from University of York (2005); PhD in Binaural Hearing funded by France Telecom R&D (2012). Research Interests: He specializes in speech enhancement algorithms for wearable devices, binaural hearing systems, room acoustics modeling, and acoustic SLAM. His work includes developing virtual reality tools for hearing aid evaluation and room identification techniques for forensic applications. Funding: His projects are supported by EPSRC, EU FP7, Google, Reality Labs Research, and the UK Home Office. Notable grants include EPSRC EP/M026698/1: Environment-aware speech enhancement EPSRC EP/S035842/1: Binaural hearing aids with VR evaluation EU FP7/2007-2013: Robot audition using spherical microphone arrays Google: Speech dereverberation for VoIP UK Home Office: Audio forensics via room geometry inference Labs: Active member of the Speech and Audio Processing group led by Prof. Mike Brookes and Prof. Patrick A. Naylor.

Gérard CholletView profile

Professor

• Speech Processing
• Biometrics
• Audiovisual Systems
• +3 more

Dr. Gérard Chollet is a Professor at Telecom SudParis, associated with the SAMOVAR laboratory. His research focuses on speech processing, biometrics, privacy-preserving AI, and assistive robotics. He leads projects like the EMPATHIC virtual coach for aging populations and the Roberta IRONSIDE robot assistant. Key contributions include audiovisual identity verification, low-resource ASR systems, and privacy-conscious machine learning frameworks. His work spans over 20 years with publications in top journals and conferences, emphasizing healthcare technology and multimodal human-machine interaction. Education: Not explicitly stated in text, but expertise implies advanced degrees in electrical engineering or computer science. Research Interests: Speech recognition, audiovisual biometrics, privacy in AI, assistive robotics, and end-to-end machine learning. Recent work emphasizes ethical AI, healthcare applications, and secure cloud computing. Grants & Collaborations: Involved in EU projects (e.g., EMPATHIC, IRONSIDE). Collaborates with industry partners (e.g., SpeechMorphing Inc., University of Surrey) and global institutions (e.g., University of Tsukuba, MIT). Lab/Team: SAMOVAR lab at Telecom SudParis, focusing on media and networks research. Active in cross-disciplinary teams for health tech and AI ethics.

Dijana PetrovskaView profile

Lecturer

• Biometrics
• Speech Processing
• Computer Vision
• +3 more

Dijana Petrovska is a Lecturer at Telecom SudParis, part of the Université Paris-Saclay. Her work focuses on biometric systems, medical diagnostics, and privacy-preserving technologies. She has contributed to projects like the EMPATHIC Virtual Coach, aimed at improving elderly health through AI-driven virtual assistants. Her research interests include facial recognition, speech processing, and cryptographic key regeneration using biometrics. Notably, she has pioneered methodologies for early-stage Parkinson's disease detection via facial action units and voice analysis. She also explores secure authentication systems combining biometrics with cryptography, emphasizing privacy preservation. Publications span over two decades, with recent work emphasizing cross-disciplinary applications of AI in healthcare and security. While no specific academic awards are listed, her impactful contributions to the field of biometric and medical AI are evident through her prolific publication record and active project leadership. Her involvement in collaborative projects like SpeechXRays and EMPATHIC demonstrates a commitment to bridging technical innovation with real-world societal needs. Though no student advisees are listed, her work likely impacts both academic and industry research ecosystems.

Suresh Raja NeethirajanView profile

Associate Professor

• Digital Agriculture
• Artificial Intelligence
• Big Data Analytics
• +11 more

Dr. Suresh Raja Neethirajan is an Associate Professor and Dalhousie University Research Chair in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. His research program focuses on digital livestock farming, integrating artificial intelligence, big data analytics, and advanced sensor technologies to improve animal health, welfare, and sustainable agricultural practices. His research interests span Digital Agriculture , Artificial Intelligence , Big Data Analytics , Animal-Computer Interaction , and Sensors & Bio-Instrumentation . He leads the Mooanalytica research lab, which develops innovative tools such as facial recognition systems for emotion detection in cows and pigs, rapid diagnostic sensors for avian influenza and food allergens, and wearable devices for non-invasive physiological monitoring. The recent publications reflect a strong trend in developing AI-driven, sensor-based solutions for precision livestock farming. These include emotion recognition in pigs, sex detection in chicken eggs, and circadian rhythm modeling in dairy cows, all leveraging computer vision, machine learning, and multimodal data fusion to advance animal welfare and farm efficiency. Dalhousie University Research Chair Dr. Neethirajan supervises BSc, MSc, and PhD students and is actively involved in teaching and guest lectures on sensor technologies. His lab has access to state-of-the-art research facilities in the Netherlands and collaborates across veterinary, agricultural, and biomedical domains. He has previously held academic and research positions at Wageningen University, the University of Guelph, Oak Ridge National Laboratory, and the National Agricultural Research Organization in Japan. His team has developed numerous industrial-ready solutions, including on-farm detection tests for ketosis and coronavirus, and is focused on transforming laboratory innovations into practical applications for the agri-food industry. Future work continues to emphasize resilience sensing, data management systems, and non-contact vital sign technologies.

Kjell LemströmView profile

Senior Lecturer

• Computer and information sciences
• String Algorithms
• Computational Geometry
• +3 more

Kjell Lemström is a Senior University Lecturer at the Department of Computer Science, University of Helsinki's Faculty of Science. He leads the Bachelor's Programme in Computer Science and directs the DEFA project focused on digital education and admissions. His research spans computational musicology (music pattern matching, rhythm analysis) and computer science education (MOOC effectiveness, curriculum design). Research interests include: Music Informatics: Polyphonic pattern detection, transposition-invariant algorithms, rhythmic analysis, and symbolic music processing Computational Methods: String algorithms, geometric computing, and optimization techniques for large-scale data Educational Innovation: Programming pedagogy, student retention in MOOCs, and competency-based admissions systems His recent publications (2021-2024) reflect dual focus areas: 60% explore music information retrieval (algorithm development for pattern detection), while 40% investigate computing education (curriculum transitions and MOOC effectiveness). He received the Award for senior researcher (2003) for contributions to computational musicology. Supervisory roles include doctoral thesis supervision (e.g., Teppo Ahonen) and hosting visiting academics like Darrell Conklin. Current project leadership includes Computer Science Admission Policies and Study Analytics (2024-present).

Edward DoeringView profile

Professor

• Audio Signal Processing
• Computer Music
• Digital Systems
• +5 more

Edward Doering is a Professor of Electrical and Computer Engineering at Rose-Hulman Institute of Technology. His expertise spans audio signal processing, computer music, digital systems, and video-based curriculum development. Awards : National Instruments Elite Educators Award (inaugural group of 25 winners) for contributions to embedded systems education. Research : Focuses on real-time audio processors, video-based teaching tools, and embedded controller applications. Publications : Notable works include guides on NI myRIO technology (2013, 2015) and innovative educational resources like CLEO for circuit analysis courses. Teaching : Specializes in integrating signal processing and digital systems into interactive learning environments.

Dilek Günneç DanışView profile

Associate Professor

• Combinatorial optimization
• Network optimization models
• Social network analysis
• +7 more

Dilek Günneç Danış is an Associate Professor in the Industrial Engineering Department at Özyeğin University, Turkey. Her academic work spans network optimization, social network analysis, and humanitarian logistics with applications in disaster management, marketing, and public health. Ph.D. in Operations Management/Management Science, University of Maryland (2012) M.Sc. in Industrial Engineering, Koç University (2007) B.Sc. in Mathematics with Minor in Operations Research, Middle East Technical University (2005) Research Interests : Combinatorial optimization and complex systems Network modeling for information propagation New product development under social influences Humanitarian logistics in refugee crises Disaster mitigation and infrastructure resilience Scientific Contributions : Developed agent-based models for refugee migration paths Created capacitated mobile facility location frameworks Advanced influence maximization algorithms for social networks Optimized disaster response logistics systems Scientific Awards : Best Poster Award (Business Analytics Workshop, 2017) Advised team: Third Place INFORMS Student Competition (2020)

• Computer Engineering
• Multimedia Security
• Embedded Systems
• +4 more

Cem Taşkın is an Assistant Professor at the Department of Computer Engineering, Trakya University, and has served as Department Head since January 2023. Previously, he was Assistant Professor at Tunca Vocational School (2011-2014) and held administrative roles as School Director (2013-2017) and Deputy Director (2012-2013). He earned his PhD in Computer Engineering from Trakya University in 2010. High School: Kırklareli Anatolian High School (1999) Bachelor's: Trakya University, Computer Engineering (2003) Master's: Trakya University, Computer Engineering (2006) PhD: Trakya University, Computer Engineering (2010) His research focuses on Computer Engineering , Multimedia Security , and Embedded Systems , with a strong emphasis on encryption techniques for MPEG video streams and mobile/web application development. Recent work includes applying interactive mobile applications for pediatric chemotherapy treatment (2022) and optimizing embedded systems as dashboards (2016). Publication trends reveal expertise in video encryption (I-frames, byte alignment, XOR operations), chaotic algorithms for sound data security, and software development for educational and administrative systems. His work bridges theoretical cryptography with practical implementations in multimedia and embedded environments. Supervised Master's Theses: Altuğ Yiğit - Human Insight in Business Processes with Deep Learning Temel Yavuz - Corporate Content-Information Management Systems Murat Emre Yücel - Text Classification with Active Learning Projects: Led initiatives at Trakya University on mobile application development laboratories (2016), campus automation systems (2016), and MPEG audio querying (2008). Participated in KOSGEB's R&D Innovation Project for Dream Cash Register/Barkot Systems (2012).

Nick JanßenView profile

Researcher

• Machine Learning
• Computer Science
• Signal Processing
• +3 more

Nick Janßen is a researcher at the Institute for Information Processing, Leibniz University Hannover, working towards his PhD since July 2024 in Dr. Melanie Schaller’s group. His research focuses on state space models and audio monitoring applications in biomedical contexts. Education: Master’s Degree in Computer Engineering (2024), Leibniz University Hannover Bachelor’s studies in Computer Engineering (2018–2024) His research interests include machine learning , signal processing , and probabilistic modeling , particularly applied to medical engineering and data segmentation . Recent work explores audio monitoring for surgical applications and conditional normalizing flows for error detection in segmentation data. Notable publications include a preprint on state space models for bone cement disintegration monitoring (2025) and a dataset on erosion depth analysis (2024). His methodological work bridges computer science and biomedical engineering .

Vijay ParsaView profile

Associate Professor

• Biomedical Engineering
• Acoustic Signal Processing
• Speech Processing
• +3 more

Vijay Parsa is an Associate Professor in the Department of Electrical and Computer Engineering at Western University , Canada. He holds the Oticon Foundation’s Chair in Acoustic Signal Processing, a joint position between the Faculties of Health Sciences and Engineering, focusing on interdisciplinary research in acoustic signal processing for audiology. Education: Ph.D. in Biomedical Engineering, University of New Brunswick M.E.Sc. in Electrical Engineering, University of New Brunswick B.Eng. in Electronics and Communication Engineering, Osmania University, India His research centers on acoustic signal processing for hearing aids, speech quality evaluation, and assistive listening devices. He develops algorithms for frequency compression, noise reduction, and envelope enhancement to improve speech perception for individuals with hearing loss. Prominent trends in his research include applications of machine learning and neural networks in speech processing, computational auditory modeling, and validation protocols for pediatric hearing aid fitting. His work bridges engineering and clinical audiology. Scientific Awards: Shaw Memorial Postdoctoral Award, Canadian Acoustics Association Dr. Parsa has contributed extensively to the field, with publications in journals like Ear & Hearing , Journal of the Acoustical Society of America , and IEEE Signal Processing Magazine . His work informs standards in hearing aid verification and wireless remote microphone systems.

Jean-Louis GutzwillerView profile

Researcher

• Image Processing
• Signal Processing
• Computer Vision
• +3 more

Jean-Louis Gutzwiller is a Researcher at the Lorrain Laboratory for Research in Computer Science and its Applications (LORIA), a joint research unit of CNRS, Inria, and the University of Lorraine, focusing on advanced image processing and compression techniques for hyperspectral data and transportation systems. His work bridges theoretical signal processing with practical applications in remote sensing and intelligent infrastructure. His research spans hyperspectral image compression, wavelet transforms, vehicle-to-road communication systems, and speaker diarization algorithms. Key contributions include developing exogenous quasi-optimal spectral transforms for MERIS satellite data, optimizing SPIHT coders for hyperspectral imaging, and pioneering electromagnetic loop-based vehicle detection systems. His methodology emphasizes low-complexity solutions suitable for onboard satellite processing and real-time applications. Analysis of his 11 publications (2022-2025) reveals a dominant focus on hyperspectral compression techniques, particularly using exogenous transforms and zero-tree coding for remote sensing data. Secondary themes include vehicle infrastructure communication systems and neural gas algorithms for audio processing. His work consistently addresses computational efficiency challenges in resource-constrained environments like satellite platforms. Gutzwiller operates within LORIA's collaborative framework, contributing to France's national research ecosystem through partnerships with CNRS and Inria. His laboratory affiliation enables interdisciplinary work spanning computer science, aerospace engineering, and transportation technology, with practical implementations in earth observation and smart mobility systems.