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Found 13 papers, 6 papers with code
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.
Zero-Shot Audio Classification using Image Embeddings
The results show that the classification performance is highly sensitive to the semantic relation between test and training classes and textual and image embeddings can reach up to the semantic acoustic embeddings when the seen and unseen classes are semantically similar.
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).
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.
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.
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.
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.
Active Learning for Sound Event Detection
Training with recordings as context outperforms training with only annotated segments.
Acoustic Scene Classification: A Competition Review
In this paper we study the problem of acoustic scene classification, i. e., categorization of audio sequences into mutually exclusive classes based on their spectral content.
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.
Sound Event Detection in Multichannel Audio Using Spatial and Harmonic Features
In this paper, we propose the use of spatial and harmonic features in combination with long short term memory (LSTM) recurrent neural network (RNN) for automatic sound event detection (SED) task.
Convolutional Recurrent Neural Networks for Polyphonic Sound Event Detection
Sound events often occur in unstructured environments where they exhibit wide variations in their frequency content and temporal structure.
