Convolutional Networks Used to Classify Video and Audio Data

Marcel Nikmon, Roman Budjac, Daniel Kuchár, Peter Schreiber, D. Janácová
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引用次数: 6

Abstract

Abstract Deep learning is a kind of machine learning, and machine learning is a kind of artificial intelligence. Machine learning depicts groups of various technologies, and deep learning is one of them. The use of deep learning is an integral part of the current data classification practice in today’s world. This paper introduces the possibilities of classification using convolutional networks. Experiments focused on audio and video data show different approaches to data classification. Most experiments use the well-known pre-trained AlexNet network with various pre-processing types of input data. However, there are also comparisons of other neural network architectures, and we also show the results of training on small and larger datasets. The paper comprises description of eight different kinds of experiments. Several training sessions were conducted in each experiment with different aspects that were monitored. The focus was put on the effect of batch size on the accuracy of deep learning, including many other parameters that affect deep learning [1].
用于视频和音频数据分类的卷积网络
深度学习是机器学习的一种,机器学习是人工智能的一种。机器学习描述了各种技术的组合,深度学习就是其中之一。深度学习的使用是当今世界当前数据分类实践的一个组成部分。本文介绍了使用卷积网络进行分类的可能性。针对音频和视频数据的实验显示了不同的数据分类方法。大多数实验使用众所周知的预训练AlexNet网络和各种预处理类型的输入数据。然而,也有其他神经网络架构的比较,我们也展示了小数据集和大数据集的训练结果。本文包括八种不同实验的描述。在每个实验中都进行了几次培训,对不同方面进行了监测。重点放在批大小对深度学习准确性的影响上,包括影响深度学习的许多其他参数[1]。
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