DCASE2017 Challenge Setup: Tasks, Datasets and Baseline System

By Annamaria Mesaros, Toni Heittola, Aleksandr Diment, Benjamin Elizalde, Ankit Shah, Emmanuel Vincent, Bhiksha Raj, Tuomas Virtanen. 2017

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Citations in the corpus

UniAudio: An Audio Foundation Model Toward Universal Audio Generation (2023, methodology): “AUDIT Wang et al. (2023d) is the SOTA model in audio edit task, which designs a data simulation strategy to get triplet training and test data ( e.g. , {audio, audio, text}).”
FSD50K: An Open Dataset of Human-Labeled Sound Events (2020, background): “An early example of this approach is TUT Rare Sound Events 2017 [81].”
PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition (2019, background): “Recently, the Detection and Classification of Acoustic Scenes and Events (DCASE) challenge series [2], [7]–[9] have provided publicly available datasets, such as acoustic scene classification and sound event detection datasets.”
The Sound of Pixels (2018, background): “While auditory scene analysis [1] is widely studied in the fields of environmental sound recognition [2,3] and source separation [4,5,6,7,8,9], the natural synchronization between vision and sound can provide a rich supervisory signal for grounding sounds in vision [10,11,12].”
VOICe: A Sound Event Detection Dataset For Generalizable Domain Adaptation (2019, background): “events from TUT Rare Sound Events 2017 development set [18] and acoustic scenes from TAU Urban Acoustic Scenes 2019 development set [19].”
Audio Set Classification with Attention Model: A Probabilistic Perspective (2017, background): “Audio classification has attracted significant interests in recent years, such as the Detection and Classification of Acoustic Scenes and Events (DCASE) challenge [1, 2].”
Acoustic Scene Classification by Ensembling Gradient Boosting Machine and Convolutional Neural Networks (2017, background): “In particular, DCASE 2017 challenge comprises four tasks: acoustic scene classification (task 1), detection of rare sound events (task 2), sound event detection in real life audio (task 3), and large-scale weakly supervised sound event detection for smart cars (task 4) [2].”
VideoLLaMA 2: Advancing Spatial-Temporal Modeling and Audio Understanding in Video-LLMs (2024, background): “AQA For the AQA task, we select open-ended Clotho-AQA (Lipping et al., 2022a) and multiple-choice TUT2017 (Mesaros et al., 2016) dataset as our benchmark and report accuracy as the evaluation metric.”
General-purpose Tagging of Freesound Audio with AudioSet Labels: Task Description, Dataset, and Baseline (2018, methodology): “In recent years, the various editions of the DCASE Challenge have provided scenarios for evaluating different computational methods for several sound recognition tasks using common publicly available datasets [2].”
Adaptive Pooling Operators for Weakly Labeled Sound Event Detection (2018, methodology): “2) DCASE 2017 Task 4: The DCASE 2017 challenge [10] consisted of four tasks, including one task with the same prob-”
A Framework for the Robust Evaluation of Sound Event Detection (2019, methodology): “SED has received increasing research interest from both academia and industry, as evidenced by the growing number of participants to the Detection and Classification of Acoustic Scenes and Events (DCASE) data challenge since its start in 2013 [4–8].”
Multi-label Few-shot Learning for Sound Event Recognition (2019, background): “The task has essential applications in many fields such as surveillance, smart city, and smart cars [1], [2].”
AudioCaps: Generating Captions for Audios in The Wild (2019, background): “There exist a number of datasets to aid in achieving this goal, including DCASE series (Stowell et al., 2015; Mesaros et al., 2016, 2017), UrbanSound8k (Salamon et al., 2014), ESC (Piczak, 2015).”
Multi-level attention model for weakly supervised audio classification (2018, background): “The detection and classiﬁcation of acoustic scenes and events (DCASE) challenge 2013, 2016, 2017 [1, 2, 6] datasets are comprised of several hours of data.”
Active Learning for Sound Event Detection (2020, methodology): “rectly learn SED models, such as in Detection and Classification of Acoustic Scenes and Events (DCASE) 2017 task 4 subtask B [20].”