جست‌وجوی استاد و پژوهشگر

Swati Chandnaمشاهده پروفایل

مدرس ارشد

• Statistics
• Data Science
• Network Theory
• +۳ مورد دیگر

Dr. Swati Chandna is a Senior Lecturer at the School of Computing and Mathematical Sciences, Birkbeck, University of London. She holds an honorary position as an Honorary Lecturer in Statistics at University College London (UCL) from January 2023 to January 2026. She earned her PhD in Statistics from Imperial College London in 2013. Her research focuses on statistical modeling, network analysis, and bioinformatics, with notable contributions to stochastic networks, single-cell genomic data analysis, and complex-valued signal processing. Teaching responsibilities include modules such as Bayesian Methods, Analysing Data, Statistical Analysis, and Project Applied Statistics. She serves as Admissions Tutor for Graduate Certificate and Diploma in Statistics for Data Science and as School Ethics Lead at Birkbeck. Her work bridges theoretical statistics with practical applications in genomics, environmental modeling, and biomedical research. Dr. Chandna’s recent research explores topics like covariate-driven network estimation, stochastic modeling of genomic data, and bootstrap techniques in source separation. Her publications reflect interdisciplinary collaboration across statistics, computer science, and life sciences.

Stefan Bleeckمشاهده پروفایل

استاد

• Hearing Science
• Audiology
• Signal Processing
• +۵ مورد دیگر

Professor Stefan Bleeck is a Professor of Hearing Science and Technology at the University of Southampton, leading the Hearing and Balance Centre and directing the Institute of Sound and Vibration Research (ISVR). His research focuses on the intersection of hearing science, audiology, and signal processing, with specialties in bio-inspired auditory modeling, speech intelligibility in noise, cochlear implants, and auditory evoked potentials. He holds a PhD in computational neuroscience and has held roles including Head of the Hearing and Balance Centre. Awards include Vice-Chancellor's Teaching Awards (2009) and Google Research Awards (2012). Education: Diploma in Physics (University of Darmstadt, 1995), PhD in Computational Neuroscience (University of Darmstadt, 2000). Research spans experimental, computational, and clinical approaches to improve hearing aids and cochlear implants. Active projects include developing speech enhancement algorithms, antiphasic speech tests for hidden hearing loss, and neural-space speech processing. Supervises multiple PhD students in engineering and computer science. Publications highlight advancements in speech enhancement, bio-inspired models, and cross-linguistic hearing tests. Collaborates with institutions like Google and the European Union on projects funded by EPSRC, Cancer Research UK, and others. His work aims to enhance speech understanding for hearing-impaired individuals through innovative signal processing and auditory modeling.

Michael Clarkeمشاهده پروفایل

استاد

• Computer Music
• Electroacoustic Music
• Music Technology
• +۶ مورد دیگر

Professor Michael Clarke is a distinguished academic at the University of Huddersfield , serving as Director of IRiMaS (Interactive Research in Music as Sound) and holding leadership roles including former Dean of the School of Music, Humanities and Media and Dean of the Graduate School . His career spans over three decades at Huddersfield, where he has pioneered innovative software for music composition, pedagogy, and analysis. Education: PhD in Music (Durham University), MTC in Teaching (UCL Institute of Education) Clarke's research focuses on composition , particularly in live interactive works , and the development of software like Max/MSP for musicological analysis and sound synthesis. His work intersects with UN Sustainable Development Goals , emphasizing technological innovation in education and cultural preservation. Recent publications highlight his contributions to interactive aural analysis and fluid corpus manipulation tools. Clarke has secured major funding, including a €2.5m ERC Advanced Grant for IRiMaS and AHRC grants for collaborative projects. Awards include the National Teaching Fellowship (2011) and multiple European Academic Software Awards . Scientific Awards: National Teaching Fellowship (2011) European Academic Software Awards (record 3 wins) As a Principal Investigator , he has led projects with Prof Peter Manning and Dr Frédéric Dufeu, while actively supervising PhD students and contributing to REF assessments.

Guy Brownمشاهده پروفایل

استاد

• Computational Auditory Scene Analysis
• Speech Recognition
• Auditory Modelling
• +۳ مورد دیگر

Professor Guy Brown is Chair of Computer Science at the University of Sheffield's School of Computer Science. He holds a BSc in Applied Science (1984), PhD in Computer Science (1992), and MEd in Teaching and Learning (1997). His research focuses on Computational Auditory Scene Analysis (CASA), noise-robust speech recognition, auditory modeling, and binaural processing. Research interests include: Machine hearing systems for sound source separation Reverberation-robust speech processing Auditory scene analysis models for normal/impaired hearing Applications in robotics and healthcare technologies Publication trends show recent focus on deep learning approaches for biomedical applications including sleep apnea detection, respiratory sound analysis, and multimodal health monitoring systems using neural networks. Honors include: University Senate Award for Excellence in Teaching (2014) Microsoft Software Engineering Innovation Award (2013) He leads doctoral supervision for 15+ students and has secured research funding from EPSRC, Innovate UK, EU FP7, and AHRC. Manages the Speech and Hearing research group and has held visiting positions at international institutions including LIMSI-CNRS and ATR Japan.

Jon Barkerمشاهده پروفایل

استاد

• Noise-Robust Speech Processing
• Machine Listening
• Computational Modeling of Human Hearing
• +۴ مورد دیگر

Professor Jon Barker is a faculty member at the University of Sheffield , where he holds a Personal Chair in the School of Computer Science . He leads the Speech and Hearing (SpandH) research group and co-founded the CHiME international workshop series on robust speech recognition. Education : PhD in Computer Science (University of Sheffield, 1999); BA in Electrical and Information Sciences (Cambridge University). Research Focus : His work bridges machine listening and human auditory perception , with key contributions to noise-robust speech recognition , speech intelligibility prediction , and hearing aid signal processing for speech and music. Recent projects include the Clarity Challenges and Cadenza Challenges , large-scale machine learning initiatives to improve accessibility for hearing-impaired users. Publication Trends : Recent articles emphasize machine learning for hearing aid optimization , dysarthric speech recognition , audio-visual integration , and music demixing algorithms . Collaborations span speech processing, psychoacoustics, and biomedical engineering. Scientific Awards : EURASIP Best Paper Award (2009) ISCA Best Paper Award (2008) Grants and Leadership : He has secured major EPSRC grants including EnhanceMusic (2022-2026) and Challenges to Revolutionise Hearing Device Processing (2019-2025). He co-led the TAPAS Marie Curie Training Network (2017-2022) and led projects like AV-COGHEAR (2015-2018) and CHiME (2009-2012). Labs and Teams : Barker collaborates closely with the Speech and Hearing Research Group and contributes to international initiatives like the CHiME Workshop . His lab develops open datasets such as the Clarity Speech Corpus and Audio-Visual Lombard Corpus .

Ke Chenمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Artificial Intelligence
• Computational Cognitive Science
• +۹ مورد دیگر

Dr. Ke Chen is a Senior Lecturer in the School of Computer Science at The University of Manchester, leading the Machine Learning and Perception (MLP@UoM) Lab. His research focuses on machine learning, deep learning, reinforcement learning, and their applications in intelligent systems, computer vision, and audio/speech processing. He has supervised over 50 PhD students and holds editorial roles in journals like Neural Networks and IEEE Transactions on Neural Networks . Dr. Chen has received awards such as the NSFC Distinguished Principal Young Investigator Award (2001) and JSPS Research Award (1993). Education: PhD in Computer Science (1990), with academic positions at institutions including Peking University, The Ohio State University, and Kyushu Institute of Technology. His professional activities include roles in IEEE Computational Intelligence Society committees and external examiner roles at universities like Essex. Research interests span computational cognitive systems, biometric authentication, and video game AI. Key contributions include advancements in deep architectures, reinforcement learning, and speaker-specific feature extraction. His lab, MLP@UoM, explores topics like explainable AI and transfer learning. Recent publications (2024) include work on goal-conditioned reinforcement learning and bias-resilient algorithms. He is actively involved in international conferences, serving as a keynote speaker and program committee member.

• AI for Sound
• Machine Learning
• Signal Processing
• +۴ مورد دیگر

Mark Plumbley is a Professor of Signal Processing at the Centre for Vision, Speech and Signal Processing (CVSSP) within the School of Computer Science and Electronic Engineering at the University of Surrey. He holds an EPSRC Fellowship in 'AI for Sound' and has led major research initiatives, including the DCASE challenges. His work focuses on AI-driven analysis of acoustic scenes and events, with contributions to machine learning, audio source separation, and sparse representations. Previously, he was Director of the Centre for Digital Music at Queen Mary University of London and Head of the School of Computer Science at Surrey. Education: PhD in Neural Networks (1991). Academic roles include Professorships at King’s College London (1991–2002) and Queen Mary University of London (2002–2014). Research spans audio event detection, sound scene classification, and generative AI for audio synthesis. He leads projects like the EPSRC-funded 'Making Sense of Sounds' and 'Musical Audio Repurposing using Source Separation', and co-edited the Springer book on Computational Analysis of Sound Scenes and Events. Research Interests: AI for Sound: Machine learning applied to real-world audio analysis. Acoustic Scene and Event Recognition: Developing models for sound classification and localization. Generative Audio Models: Text-to-audio systems and diffusion models for sound synthesis. Healthcare Applications: Audio-based diagnostics and bioacoustic signal processing. Grants and Awards: EPSRC Fellowships, EU-funded networks (SpaRTaN, MacSeNet), and Fellowships from IET and IEEE. Notable awards include the IEEE Young Author Best Paper Award (co-authored with students) and leadership in the DCASE community. Labs and Collaborations: CVSSP at Surrey, collaborations with BBC R&D, and interdisciplinary projects on urban soundscapes and noise pollution (UK Acoustics Network Plus).

• Signal Processing
• Computer Vision
• Machine Learning
• +۶ مورد دیگر

Miroslaw Bober is Professor of Video Processing at the University of Surrey, where he joined in 2011. He leads the Visual Media Analysis team within the Centre for Vision, Speech and Signal Processing (CVSSP) in the School of Computer Science and Electronic Engineering. His extensive industry experience includes 15 years as General Manager of the Mitsubishi Electric R&D Centre Europe and Head of Research for its Visual & Sensing Division. BSc and MSc in Electrical Engineering from AGH University of Science and Technology, Krakow, Poland (1990) MSc in Machine Intelligence with distinction from Surrey University (1991) PhD in Computer Vision from Surrey University (1995) Professor Bober's research focuses on novel techniques in signal processing, computer vision and machine learning with applications in industry, healthcare, big-data and security. His expertise particularly lies in image and video analysis and retrieval, including visual search, object recognition, and analysis of motion, shape and texture. His algorithms for shape analysis, image/video fingerprinting, and visual search are considered world-leading and have been selected for ISO International standards within MPEG, with applications used by organizations like the Metropolitan Police. His recent publication trends show a strong focus on hybrid network architectures, scene graph generation, medical imaging applications, and augmented reality publishing systems. His work spans both theoretical advancements in computer vision and practical implementations addressing real-world challenges in media, healthcare, and security domains. The research demonstrates a consistent pattern of bridging academic innovation with industrial applications, particularly in visual search technology and media analysis. Presidential Award for strengthening the TV business in Japan via innovative 'Visual Navigation' content access technology (2010) Mitsubishi Best Invention Award for Image Signature Technology (2008) Professor Bober serves as Programme Director for the MSc in Multimedia Signal Processing and Communications and holds various teaching and mentoring roles. He has secured over 30 research and industrial grants totaling more than £16M, including the BRIDGET FP-7 project (5.28 M€) as coordinator and PI, and the CODAM project (£1.05 M) as PI. His work with the BBC, Huawei, and other industry partners demonstrates strong industry-academia collaboration. As chair of MPEG technical work on Compact Descriptors for Visual Search (CDVS) and Compact Descriptors for Video Analysis (CDVA), Professor Bober leads international standardization efforts. His Visual Media Analysis team develops cutting-edge visual search and media analysis algorithms with applications across broadcast, security, and healthcare domains.

• Ethnomusicology
• Hindustani Classical Music
• Rhythmic Analysis
• +۴ مورد دیگر

Professor Martin Clayton is a leading scholar in Ethnomusicology at Durham University's Department of Music. He holds a BA in Music/Hindi (SOAS, 1988) and a PhD in Ethnomusicology (SOAS, 1993). His work focuses on Hindustani classical music, rhythmic analysis, and embodied musical interaction. He has directed major research projects like the EU-funded EnTimeMent and AHRC-funded 'Interpersonal Entrainment in Music Performance.' Clayton is a Fellow of the British Academy (2020) and former editor of the British Journal of Ethnomusicology. His research explores cross-cultural musicology, computational analysis of performance, and rhythmic entrainment across global traditions. Education: Bachelor of Arts in Music and Hindi (SOAS, University of London, 1988) Doctor of Philosophy in Ethnomusicology (SOAS, University of London, 1993) Research Interests: Clayton’s work bridges ethnomusicology with computational methods, focusing on rhythmic structures in Indian classical music, embodied performance practices, and cross-cultural musical interactions. His studies analyze gesture-sound relationships, temporal frameworks in rag performance, and the social dimensions of musical entrainment. Recent projects integrate machine learning and computer vision to classify ragas and analyze performer movements in ensemble contexts. Publications Overview: His 15+ years of research span theoretical works (e.g., Time in Indian Music , 2000), edited volumes (e.g., Experience and Meaning in Music Performance , 2013), and interdisciplinary studies combining ethnography with computational analysis. Recent work emphasizes multimodal data collection to study synchronization in global music traditions, including Japanese gagaku and Indian khyal. Awards: British Academy Fellowship (2020), Leverhulme Trust Major Research Fellowship Advising & Grants: Supervised doctoral students focusing on Indian music, global guitar practices, and embodied music interaction. He led a £1.2M AHRC grant (2016–2018) on interpersonal entrainment and co-investigated projects on Khyal music and South Asian cultural networks. Labs/Teams: Collaborates with interdisciplinary teams on projects like the Interpersonal Entrainment in Music Performance (IEMP) initiative, developing tools such as the movementsync R package for synchrony analysis. Active in global music research networks, including ESEM and the British Forum for Ethnomusicology.

Andrew McPhersonمشاهده پروفایل

استاد

• Music Computing
• New Interfaces for Musical Expression
• Digital Signal Processing
• +۳ مورد دیگر

Andrew McPherson is a Professor of Musical Interaction at Queen Mary University of London, affiliated with the School of Electronic Engineering and Computer Science and the Centre for Multimodal AI. He leads the Augmented Instruments Laboratory, a research team focused on music technology and interdisciplinary collaboration. His work bridges electrical engineering, composition, and human-computer interaction, emphasizing the creation of new tools for musicians through hardware/software interfaces and expressive performance modeling. McPherson's research interests include augmented instruments, digital signal processing, and the design of intuitive musical interfaces. He has pioneered projects like the Bela embedded platform and the Magnetic Resonator Piano, emphasizing practical applications in traditional and experimental music venues. Collaborations with artists and industry inform his approach, ensuring research outputs are artistically relevant and accessible. Education & Expertise: Trained in electrical engineering and composition, McPherson combines technical proficiency with artistic sensibility. His lab’s projects, such as the TouchKeys and hackable instruments, highlight his focus on democratizing music technology. Grants & Awards: He holds a Royal Academy of Engineering Senior Research Fellowship (2021–2026) and an ERC Consolidator Grant (2023–2027). He co-leads the UKRI Centre for Doctoral Training in Artificial Intelligence and Music, fostering future researchers in AI-driven music innovation. Labs/Teams: The Augmented Instruments Lab collaborates across disciplines, with dual affiliation at Queen Mary and Imperial College London’s Dyson School of Design Engineering. The lab’s work spans from foundational research to industry partnerships, including spinout company Augmented Instruments Ltd, which supports maker communities and industry consultancy.

Josh Reissمشاهده پروفایل

استاد

• Audio Engineering
• Signal Processing
• Music Production
• +۴ مورد دیگر

Josh Reiss is a Professor of Audio Engineering at Queen Mary University of London (QMUL), part of the School of Electronic Engineering and Computer Science . He holds additional roles including President-Elect and Fellow of the Audio Engineering Society (AES), and Visiting Professor at Birmingham City University. His research focuses on audio signal processing, procedural audio, and intelligent music production. He earned degrees including BSc in Physics, BSc in Mathematics, and a PhD. Research & Awards : Reiss has published over 200 papers, authored books like Intelligent Music Production , and received awards such as the AES Board of Governors Award (2009, 2010) and Best JAES Paper 2016. His work spans sound synthesis, dynamic range compression, and live audio systems. Teaching & Industry : Teaches modules like Artificial Intelligence and Sound Design. Co-founded startups LandR (AI mixing), Tonz, and Nemisindo. Leads the Centre for Digital Music at QMUL, advancing research in audio technology. Labs & Teams : Active in the Centre for Digital Music, collaborating on projects like the Open Multitrack Testbed and semantic audio evaluation tools.

Tony Stockmanمشاهده پروفایل

مدرس ارشد

• Human-Computer Interaction
• Auditory Displays
• Data Sonification
• +۳ مورد دیگر

Dr. Tony Stockman is a Senior Lecturer at Queen Mary University of London's School of Electronic Engineering and Computer Science. His teaching focuses on Database Systems, Interaction Design, and Semi-structured Data Modelling across undergraduate and postgraduate programs. His research interests include Human-Computer Interaction, Auditory Displays, and Data Sonification, with a strong emphasis on accessibility and cross-modal interfaces. Dr. Stockman has contributed to over 100 publications, exploring topics such as biofeedback systems, assistive technologies for visually impaired individuals, and collaborative design methodologies. His work frequently bridges technical innovation with user-centric design principles. Notable projects include the development of non-visual navigation tools like the Audiom web-based map viewer and co-designed haptic wearables for music synchronization. His research also addresses challenges in education technology, such as dyslexia screening through serious games.

Johan Pauwelsمشاهده پروفایل

مدرس

• Audio Signal Processing
• Music Information Retrieval
• Machine Learning for Audio
• +۳ مورد دیگر

Dr. Johan Pauwels is a Lecturer in Audio Signal Processing at Queen Mary University of London's School of Electronic Engineering and Computer Science, where he is affiliated with the Centre for Digital Music and the Centre for Multimodal AI. His educational background includes: Master of Science in Electrical/Electronics Engineering from KU Leuven (2006) Master of Science in Artificial Intelligence from KU Leuven (2007) PhD from Ghent University (2016) on automatic harmony recognition from audio Johan's research focuses on making machines understand audio to the level of a trained professional. His work combines machine learning, signal processing, data science, and music theory to develop tools for musicians, listeners, and music learners. He has been working on narrowing the gap between academic research and user-centric applications, web-based music services, and the personalization of spatial and immersive audio. His specific interests include machine learning for audio, audio signal processing, music information retrieval, and binaural audio. His recent publications show a strong focus on music representation learning, with particular attention to limited data scenarios, multimodal approaches, and spatial audio processing. His work bridges theoretical music concepts with practical machine learning applications, especially in chord recognition, beat detection, and instrument recognition. He has made significant contributions to HRTF (Head-Related Transfer Function) research and development of tools for spatial audio processing. Dr. Pauwels is actively involved in research funding, with current grants including the AIM CDT Internship with Sofilab (2025), AIM CDT Studentship - Stem (2024), and AIM CDT Internship with stem.tech (2024). He currently supervises multiple PhD students, primarily through the UKRI Doctoral School in AI and Music, with research topics spanning intelligent audio editing, neural drum synthesis, source separation, graph neural networks for music recommendation, and more. In addition to PhD supervision, he typically guides 8-10 undergraduate and 8-10 master's students through their final year projects. His teaching responsibilities include ECS7013P Deep Learning for Audio and Music (MSc/PhD level) and ECS411U Signals and Information (first-year undergraduate).

Ahsan Adeelمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Computational Neuroscience
• Multisensory Integration
• +۵ مورد دیگر

Dr. Ahsan Adeel is an Associate Professor of Cellular AI and Theoretical Neuroscience at the University of Stirling. He holds affiliations with multiple institutions including MIT, Oxford, and the Howard Brain Sciences Foundation. His research focuses on bridging cellular neurobiology with AI, particularly through the study of pyramidal Two-Point Neurons (TPNs) and their role in conscious processing. His work emphasizes developing biologically plausible AI models for healthcare, robotics, and neuromorphic computing. Education: B.Eng. in Electrical Engineering, MSc in Electronics, PhD in Cognitive Computing. Research interests include: Conscious Multisensory Integration (CMI), neuromorphic chip design, explainable AI (XAI), and applications in hearing aids, disaster management, and neurological disorders. His lab, the CMI Lab, explores how TPN mechanisms can overcome computational limitations of current AI. Key projects include a £4M EPSRC-funded hearing aid initiative, 5G-enabled environment planners for dementia patients, and collaborations with NASA/ESA on AI for space exploration. His work is featured in flagship journals like Frontiers in Computational Neuroscience and the €1.2B Human Brain Project. Awards include Fellowships from Howard Foundation, MIT, and Oxford, alongside recognition for contributions to neuromorphic computing and TPN research. He has over 30 publications and leads interdisciplinary teams across 10+ global institutions.

Tillman Weydeمشاهده پروفایل

عضو هیئت علمی

• Machine Learning
• Signal Processing
• Data Analysis
• +۵ مورد دیگر

Dr. Tillman Weyde is a Reader in the Department of Computer Science at City, University of London , where he has been employed since 2021. He leads the Machine Intelligence and Media Informatics Research Group and is a member of the Machine Learning Group . Prior to this, he served as Senior Lecturer (2009–2021) and Lecturer (2005–2009) at City, and worked as a researcher at the University of Osnabrück (2001–2005), coordinating the MUSITECH project. His academic background includes PhD in Music Technology (2002), MSc in Computer Science (1999), and MSc in Mathematics, Music, Philosophy & Pedagogy (1994), all from the University of Osnabrück. Research Focus: Machine learning and signal processing methods for data analysis with applications in finance, audio, NLP, music, health, security, and education. His recent work emphasizes inductive biases in neural networks for rule-learning, extrapolation, generalization, and interpretability. Grants & Projects: Principal Investigator for the AHRC-funded Digital Music Lab (2012–2017) and Integrated Audio-Symbolic Model of Music Similarity (2017–present). Co-investigator in Innovate UK and EPSRC projects on safer gambling ( Advancing Consumer Protection , 2015–2018) and Raven (2012–2021). Collaborations: Affiliated with the Institute of Cognitive Science (Osnabrück), Intelligent Systems Research Laboratory (Reading), and the MPEG Ad-Hoc Group on Symbolic Music Representation. Awards: Co-author of the 2000 Comenius Medal-winning educational software Computer Courses in Music Ear Training and co-editor of the Osnabrück Series on Music and Computation . Publications: Over 150 peer-reviewed works including conference papers, journal articles, and book chapters, focusing on interdisciplinary applications of machine learning in music, health, and finance. Students: Supervised 13 PhD students across topics like grammar bias in neural networks, emotion recognition from audio, extrapolation behavior in neural networks, relation-based patterns, legal text parsing, and more.