Toni Heittola
researcher
I am a doctoral researcher at Tampere University (Tampere University of Technology until 2018). In my research, I am focusing on automatic content analysis of environmental audio. More
Activity
News
DCASE2021 Challenge
Our research group is once again one of the organizing teams of the DCASE2021 challenge, and I am acting as the task coordinator for acoustic scene classification challenge task.
DCASE2020 Challenge
Our research group is once again one of the organizing teams of the DCASE2020 challenge, and I am acting as the task coordinator for acoustic scene classification challenge task.
Slides for ICASSP2019 Tutorial: 'Detection and Classification of Acoustic Scenes and Events'
Final slides for a tutorial at ICASSP 2019 on "Detection and Classification of Acoustic Scenes and Events" are now available here. Jupyter notebooks for the Python examples are available at Github repository.
ICASSP2019 Tutorial: 'Detection and Classification of Acoustic Scenes and Events'
I will be presenting a tutorial at ICASSP 2019 on "Detection and Classification of Acoustic Scenes and Events", together with Tuomas Virtanen and Annamaria Mesaros, on Monday 13.5.2019
Latest publications
A curated dataset of urban acoustic scenes for audio-visual scene analysis
Overview and Evaluation of Sound Event Localization and Detection in DCASE 2019
Acoustic scene classification in DCASE 2020 Challenge: generalization across devices and low complexity solutions
Active Learning for Sound Event Detection
Ympäristömelun mittaaminen käyttäen automaattista lähteiden tunnistusta
Acoustic Scene Classification in DCASE 2019 challenge: closed and open set classification and data mismatch setups
Sound event detection
Automatic sound event detection aims at processing the continuous acoustic signal and converting it into symbolic descriptions of the corresponding sound events present at the auditory scene. Sound event detection can be utilized in a variety of applications, including context-based indexing and retrieval in multimedia databases, unobtrusive monitoring in health care, and surveillance.
Acoustic scene classification
Context recognition can be defined as the process of automatically determining the context around a device. Information about the context will enable wearable devices to provide better service to users' needs, e.g., by adjusting the mode of operation accordingly.
Auditory scene synthesis
Auditory scene synthesis aims to create a new arbitrary long and representative audio ambiance for a location by using a small amount of audio recorded at the exact location. By adding this audio ambiance for the specific location in virtual location-exploration services (e.g. Google Street view) would enhance the user experience, giving the service a more 'real' feeling.
Musical instrument recognition
Understanding the timbre of musical instruments or drums are an important issue for automatic music transcription, music information retrieval and computational auditory scene analysis.