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Annamaria Mesaros
Researcher at Tampere University of Technology
Publications - 86
Citations - 4188
Annamaria Mesaros is an academic researcher from Tampere University of Technology. The author has contributed to research in topics: Computer science & Computational auditory scene analysis. The author has an hindex of 25, co-authored 77 publications receiving 3219 citations. Previous affiliations of Annamaria Mesaros include Technical University of Cluj-Napoca & Nokia.
Papers
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Proceedings ArticleDOI
TUT database for acoustic scene classification and sound event detection
TL;DR: The recording and annotation procedure, the database content, a recommended cross-validation setup and performance of supervised acoustic scene classification system and event detection baseline system using mel frequency cepstral coefficients and Gaussian mixture models are presented.
Journal ArticleDOI
Metrics for Polyphonic Sound Event Detection
TL;DR: This paper presents and discusses various metrics proposed for evaluation of polyphonic sound event detection systems used in realistic situations where there are typically multiple sound sources active simultaneously.
DCASE 2017 challenge setup: tasks, datasets and baseline system
Annamaria Mesaros,Toni Heittola,Aleksandr Diment,Benjamin Elizalde,Ankit Shah,Emmanuel Vincent,Bhiksha Raj,Tuomas Virtanen +7 more
TL;DR: This paper presents the setup of these tasks: task definition, dataset, experimental setup, and baseline system results on the development dataset.
Journal ArticleDOI
Detection and Classification of Acoustic Scenes and Events: Outcome of the DCASE 2016 Challenge
Annamaria Mesaros,Toni Heittola,Emmanouil Benetos,Peter Foster,Mathieu Lagrange,Tuomas Virtanen,Mark D. Plumbley +6 more
TL;DR: The emergence of deep learning as the most popular classification method is observed, replacing the traditional approaches based on Gaussian mixture models and support vector machines.
Posted Content
A multi-device dataset for urban acoustic scene classification.
TL;DR: The acoustic scene classification task of DCASE 2018 Challenge and the TUT Urban Acoustic Scenes 2018 dataset provided for the task are introduced, and the performance of a baseline system in the task is evaluated.