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DCASE2017 Challenge Setup: Tasks, Datasets and Baseline System
TLDR
This paper presents the setup of these tasks: task definition, dataset, experimental setup, and baseline system results on the development dataset.
TUT database for acoustic scene classification and sound event detection
TLDR
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.
Metrics for Polyphonic Sound Event Detection
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
A multi-device dataset for urban acoustic scene classification
TLDR
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.
Detection and Classification of Acoustic Scenes and Events: Outcome of the DCASE 2016 Challenge
TLDR
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.
Acoustic event detection in real life recordings
TLDR
A system for acoustic event detection in recordings from real life environments using a network of hidden Markov models, capable of recognizing almost one third of the events, and the temporal positioning of the Events is not correct for 84% of the time.
Acoustic Scene Classification in DCASE 2020 Challenge: Generalization Across Devices and Low Complexity Solutions
TLDR
The datasets and baseline systems of Task 1: Acoustic Scene Classification in the DCASE 2020 Challenge are described, requiring good generalization properties, and classification using low-complexity solutions.
Context-dependent sound event detection
TLDR
The two-step approach was found to improve the results substantially compared to the context-independent baseline system, and the detection accuracy can be almost doubled by using the proposed context-dependent event detection.
Sound Event Detection in Multisource Environments Using Source Separation
TLDR
This paper proposes a sound event detection system for natural multisource environments, using a sound source separation front-end, with a significant increase in event detection accuracy compared to a system able to output a single sequence of events.
Singer Identification in Polyphonic Music Using Vocal Separation and Pattern Recognition Methods
TLDR
It was found that vocal line separation enables robust singer identification down to 0dB and -5dB singer-to-accompaniment ratios.
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