On the Performance Improvements of Deep Learning Methods for Audio Event Detection and Classification

Abstract Purpose Brain Magnetic Resonance Images (MRIs) are essential for the diagnosis of neurological diseases. Recently, deep learning methods for unsupervised anomaly detection (UAD) have been proposed for the analysis of brain MRI. These methods rely on healthy brain MRIs and eliminate the requirement of pixel-wise annotated data compared to supervised deep learning. While a wide range of methods for UAD have been proposed, these methods are mostly 2D and only learn from MRI slices, disregarding that brain lesions are inherently 3D and the spatial context of MRI volumes remains unexploited. Methods We investigate whether using increased spatial context by using MRI volumes combined with spatial erasing leads to improved unsupervised anomaly segmentation performance compared to learning from slices. We evaluate and compare 2D variational autoencoder (VAE) to their 3D counterpart, propose 3D input erasing, and systemically study the impact of the data set size on the performance. Results Using two publicly available segmentation data sets for evaluation, 3D VAEs outperform their 2D counterpart, highlighting the advantage of volumetric context. Also, our 3D erasing methods allow for further performance improvements. Our best performing 3D VAE with input erasing leads to an average DICE score of 31.40% compared to 25.76% for the 2D VAE. Conclusions We propose 3D deep learning methods for UAD in brain MRI combined with 3D erasing and demonstrate that 3D methods clearly outperform their 2D counterpart for anomaly segmentation. Also, our spatial erasing method allows for further performance improvements and reduces the requirement for large data sets.

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Audio Event Detection Using Wireless Sensor Networks Based on Deep Learning

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering - Wireless Internet ◽

10.1007/978-3-030-06158-6_11 ◽

2019 ◽

pp. 105-115

Author(s):

Jose Marie Mendoza ◽

Vanessa Tan ◽

Vivencio Fuentes ◽

Gabriel Perez ◽

Nestor Michael Tiglao

Keyword(s):

Wireless Sensor Networks ◽

Deep Learning ◽

Sensor Networks ◽

Event Detection ◽

Wireless Sensor ◽

Audio Event

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Deep Learning for Audio Event Detection and Tagging on Low-Resource Datasets

Applied Sciences ◽

10.3390/app8081397 ◽

2018 ◽

Vol 8 (8) ◽

pp. 1397 ◽

Cited By ~ 13

Author(s):

Veronica Morfi ◽

Dan Stowell

Keyword(s):

Deep Learning ◽

Event Detection ◽

Deep Neural Networks ◽

Learning System ◽

Training Data ◽

Quality Performance ◽

Training Performance ◽

Low Resource ◽

Advantages And Disadvantages ◽

Audio Event

In training a deep learning system to perform audio transcription, two practical problems may arise. Firstly, most datasets are weakly labelled, having only a list of events present in each recording without any temporal information for training. Secondly, deep neural networks need a very large amount of labelled training data to achieve good quality performance, yet in practice it is difficult to collect enough samples for most classes of interest. In this paper, we propose factorising the final task of audio transcription into multiple intermediate tasks in order to improve the training performance when dealing with this kind of low-resource datasets. We evaluate three data-efficient approaches of training a stacked convolutional and recurrent neural network for the intermediate tasks. Our results show that different methods of training have different advantages and disadvantages.

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Saliency detection in deep learning era: trends of development

Information and Control Systems ◽

10.31799/1684-8853-2019-3-10-36 ◽

2019 ◽

pp. 10-36 ◽

Cited By ~ 2

Author(s):

M. N. Favorskaya ◽

L. C. Jain

Keyword(s):

Deep Learning ◽

Object Detection ◽

Event Detection ◽

Visual Analysis ◽

Saliency Detection ◽

Salient Object Detection ◽

Public Image ◽

Detection Methods ◽

Salient Object ◽

Salient Event

Introduction:Saliency detection is a fundamental task of computer vision. Its ultimate aim is to localize the objects of interest that grab human visual attention with respect to the rest of the image. A great variety of saliency models based on different approaches was developed since 1990s. In recent years, the saliency detection has become one of actively studied topic in the theory of Convolutional Neural Network (CNN). Many original decisions using CNNs were proposed for salient object detection and, even, event detection.Purpose:A detailed survey of saliency detection methods in deep learning era allows to understand the current possibilities of CNN approach for visual analysis conducted by the human eyes’ tracking and digital image processing.Results:A survey reflects the recent advances in saliency detection using CNNs. Different models available in literature, such as static and dynamic 2D CNNs for salient object detection and 3D CNNs for salient event detection are discussed in the chronological order. It is worth noting that automatic salient event detection in durable videos became possible using the recently appeared 3D CNN combining with 2D CNN for salient audio detection. Also in this article, we have presented a short description of public image and video datasets with annotated salient objects or events, as well as the often used metrics for the results’ evaluation.Practical relevance:This survey is considered as a contribution in the study of rapidly developed deep learning methods with respect to the saliency detection in the images and videos.

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