基于音频的深度学习框架用于BBC电视节目分类
An Audio-Based Deep Learning Framework For BBC Television Programme Classification
摘要：
本文提出了一个使用音频对BBC电视节目进行分类的深度学习框架。音频首先被转换成频谱图，这些频谱图被馈送到预先训练的卷积神经网络（CNN）中，从而获得音频记录中发生的声音事件的预测概率。然后计算预测概率和检测到的声音事件的统计，以提取表示电视节目的区别特征。最后，提取的嵌入特征被馈送到分类器中，用于将节目分类为不同的类型。我们的实验是在BBC标记的9种类型的6,160个节目的数据集上进行的。在14倍交叉验证中，我们实现了93.7%的平均分类准确率。这证明了拟议的电视节目音频分类框架的有效性。
英文摘要：
This paper proposes a deep learning framework for classification of BBC television programmes using audio. The audio is firstly transformed into spectrograms, which are fed into a pre-trained convolutional Neural Network (CNN), obtaining predicted probabilities of sound events occurring in the audio recording. Statistics for the predicted probabilities and detected sound events are then calculated to extract discriminative features representing the television programmes. Finally, the embedded features extracted are fed into a classifier for classifying the programmes into different genres. Our experiments are conducted over a dataset of 6,160 programmes belonging to nine genres labelled by the BBC. We achieve an average classification accuracy of 93.7% over 14-fold cross validation. This demonstrates the efficacy of the proposed framework for the task of audio-based classification of television programmes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
