Academic Biography
Toni Heittola is a postdoctoral research fellow at Tampere University, affiliated with the Tampere Institute for Advanced Study. He earned his Ph.D. in 2021 from Tampere University, Finland, and has been an active member of the university’s Audio Research Group since the early 2000s. His early research focused on musical genre and instrument classification, while his doctoral work significantly advanced computational auditory scene analysis, particularly in the area of multi-source sound event detection.
Dr. Heittola’s research lies at the intersection of machine learning and audio signal processing, with a focus on environmental audio analysis, sound event detection, and acoustic scene classification. He has contributed to a wide range of academic and industrial projects, including several EU- and nationally funded initiatives such as MARVEL, EVERYSOUND, and SmartSound, where he developed scalable audio recognition systems for smart cities, healthcare, and assistive technologies.
He has authored over 60 peer-reviewed publications, including 11 journal articles, 40 conference papers, and two book chapters. This academic work has received broad academic recognition, with over 8000 citations on Google Scholar (h-index: 35) and over 3400 citations on Scopus (h-index: 22). He is a prominent member of the Detection and Classification of Acoustic Scenes and Events (DCASE) community. Over the years, he has been a driving force in the organization of the DCASE Challenge, serving as task coordinator for many tasks. His contributions have played a crucial role in laying the groundwork for the challenge. He's been heavily involved in designing evaluation protocols, putting together publicly available datasets, developing baseline systems, and creating frameworks and metrics for reproducibility. Additionally, he has taken on the important role of publication chair for several editions of the DCASE Workshop.
A strong advocate for open science, Dr. Heittola has released more than ten publicly available datasets and numerous open-source tools, including sed_eval, dcase_util, and sed_vis, which support benchmarking and reproducible research. He also maintains the DCASE community website and has developed a suite of utilities to facilitate collaborative research and community engagement. His efforts have been widely recognized for advancing transparency and accessibility in scientific research. Under his continued involvement, the DCASE community has seen substantial growth in global participation and academic impact.
In 2019, he co-presented the DCASE tutorial at the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), and in 2021, co-authored the widely cited tutorial article “Sound Event Detection: A Tutorial” in the IEEE Signal Processing Magazine. His work has been recognized with multiple honors, including the IEEE Best Paper Award (2023) and a Doctoral Dissertation Award (2022). Beyond academia, Dr. Heittola has collaborated with many industry partners and contributed to several EU-funded research initiatives.