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Found 19 papers, 6 papers with code
Sound Event Detection and Localization with Distance Estimation
Sound Event Detection and Localization (SELD) is a combined task of identifying sound events and their corresponding direction-of-arrival (DOA).
Positive and negative sampling strategies for self-supervised learning on audio-video data
In Self-Supervised Learning (SSL), Audio-Visual Correspondence (AVC) is a popular task to learn deep audio and video features from large unlabeled datasets.
Class-Incremental Learning for Multi-Label Audio Classification
Experiments are performed on a dataset with 50 sound classes, with an initial classification task containing 30 base classes and 4 incremental phases of 5 classes each.
Evaluating Classification Systems Against Soft Labels with Fuzzy Precision and Recall
Classification systems are normally trained by minimizing the cross-entropy between system outputs and reference labels, which makes the Kullback-Leibler divergence a natural choice for measuring how closely the system can follow the data.
Training sound event detection with soft labels from crowdsourced annotations
In this paper, we study the use of soft labels to train a system for sound event detection (SED).
Incremental Learning of Acoustic Scenes and Sound Events
At the same time, its performance on the previous ASC task decreases only by 5. 1 percentage points due to the additional learning of the AT task.
Self-supervised learning of audio representations using angular contrastive loss
To improve the discriminative ability of feature embeddings in SSL, we propose a new loss function called Angular Contrastive Loss (ACL), a linear combination of angular margin and contrastive loss.
Binaural Signal Representations for Joint Sound Event Detection and Acoustic Scene Classification
Sound event detection (SED) and Acoustic scene classification (ASC) are two widely researched audio tasks that constitute an important part of research on acoustic scene analysis.
Low-complexity acoustic scene classification in DCASE 2022 Challenge
The provided baseline system is a convolutional neural network which employs post-training quantization of parameters, resulting in 46. 5 K parameters, and 29. 23 million multiply-and-accumulate operations (MMACs).
Self-supervised Learning of Audio Representations from Audio-Visual Data using Spatial Alignment
In addition to the correspondence, AVSA also learns from the spatial location of acoustic and visual content.
Joint Direction and Proximity Classification of Overlapping Sound Events from Binaural Audio
Finally, we propose various ways of combining the proximity and direction estimation problems into a joint task providing temporal information about the onsets and offsets of the appearing sources.
Crowdsourcing strong labels for sound event detection
Strong labels are a necessity for evaluation of sound event detection methods, but often scarcely available due to the high resources required by the annotation task.
Sound Event Detection: A Tutorial
The goal of automatic sound event detection (SED) methods is to recognize what is happening in an audio signal and when it is happening.
Low-complexity acoustic scene classification for multi-device audio: analysis of DCASE 2021 Challenge systems
The most used techniques among the submissions were residual networks and weight quantization, with the top systems reaching over 70% accuracy, and log loss under 0. 8.
Audio-visual scene classification: analysis of DCASE 2021 Challenge submissions
More importantly, multi-modal methods using both audio and video are employed by all the top 5 teams.
What is the ground truth? Reliability of multi-annotator data for audio tagging
Crowdsourcing has become a common approach for annotating large amounts of data.
Overview and Evaluation of Sound Event Localization and Detection in DCASE 2019
A large-scale realistic dataset of spatialized sound events was generated for the challenge, to be used for training of learning-based approaches, and for evaluation of the submissions in an unlabeled subset.
Acoustic scene classification in DCASE 2020 Challenge: generalization across devices and low complexity solutions
This paper presents the details of Task 1: Acoustic Scene Classification in the DCASE 2020 Challenge.
A multi-device dataset for urban acoustic scene classification
This paper introduces the acoustic scene classification task of DCASE 2018 Challenge and the TUT Urban Acoustic Scenes 2018 dataset provided for the task, and evaluates the performance of a baseline system in the task.
