Acoustic scene classification: a comprehensive survey
Acoustic scene classification (ASC) has gained significant interest recently due to its diverse
applications. Various audio signal processing and machine learning methods have been …
applications. Various audio signal processing and machine learning methods have been …
A review of deep learning based methods for acoustic scene classification
J Abeßer - Applied Sciences, 2020 - mdpi.com
The number of publications on acoustic scene classification (ASC) in environmental audio
recordings has constantly increased over the last few years. This was mainly stimulated by …
recordings has constantly increased over the last few years. This was mainly stimulated by …
Randaugment: Practical automated data augmentation with a reduced search space
Recent work on automated augmentation strategies has led to state-of-the-art results in
image classification and object detection. An obstacle to a large-scale adoption of these …
image classification and object detection. An obstacle to a large-scale adoption of these …
Learning data augmentation strategies for object detection
Much research on object detection focuses on building better model architectures and
detection algorithms. Changing the model architecture, however, comes at the cost of …
detection algorithms. Changing the model architecture, however, comes at the cost of …
Autoaugment: Learning augmentation strategies from data
Data augmentation is an effective technique for improving the accuracy of modern image
classifiers. However, current data augmentation implementations are manually designed. In …
classifiers. However, current data augmentation implementations are manually designed. In …
Autoaugment: Learning augmentation policies from data
In this paper, we take a closer look at data augmentation for images, and describe a simple
procedure called AutoAugment to search for improved data augmentation policies. Our key …
procedure called AutoAugment to search for improved data augmentation policies. Our key …
Contrastive learning with stronger augmentations
Representation learning has significantly been developed with the advance of contrastive
learning methods. Most of those methods are benefited from various data augmentations …
learning methods. Most of those methods are benefited from various data augmentations …
Unsupervised detection of anomalous sound based on deep learning and the neyman–pearson lemma
Y Koizumi, S Saito, H Uematsu… - … on Audio, Speech …, 2018 - ieeexplore.ieee.org
This paper proposes a novel optimization principle and its implementation for unsupervised
anomaly detection in sound (ADS) using an autoencoder (AE). The goal of the unsupervised …
anomaly detection in sound (ADS) using an autoencoder (AE). The goal of the unsupervised …
General-purpose tagging of freesound audio with audioset labels: Task description, dataset, and baseline
This paper describes Task 2 of the DCASE 2018 Challenge, titled" General-purpose audio
tagging of Freesound content with AudioSet labels". This task was hosted on the Kaggle …
tagging of Freesound content with AudioSet labels". This task was hosted on the Kaggle …
A new deep CNN model for environmental sound classification
Cognitive prediction in the complicated and active environments is of great importance role
in artificial learning. Classification accuracy of sound events has a robust relation with the …
in artificial learning. Classification accuracy of sound events has a robust relation with the …