文章基本信息

标题：Music detection from broadcast contents using convolutional neural networks with a Mel-scale kernel
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作者：Byeong-Yong Jang ; Woon-Haeng Heo ; Jung-Hyun Kim 等
期刊名称：EURASIP Journal on Audio, Speech, and Music Processing
印刷版ISSN：1687-4714
电子版ISSN：1687-4722
出版年度：2019
卷号：2019
期号：1
页码：1-12
DOI：10.1186/s13636-019-0155-y
出版社：Hindawi Publishing Corporation
摘要：We propose a new method for music detection from broadcasting contents using the convolutional neural networks with a Mel-scale kernel. In this detection task, music segments should be annotated from the broadcast data, where music, speech, and noise are mixed. The convolutional neural network is composed of a convolutional layer with kernel that is trained to extract robust features. The Mel-scale changes the kernel size, and the backpropagation algorithm trains the kernel shape. We used 52 h of mixed broadcast data (25 h of music) to train the convolutional network and 24 h of collected broadcast data (ratio of music of 50–76%) for testing. The test data consisted of various genres (drama, documentary, news, kids, reality, and so on) that are broadcast in British English, Spanish, and Korean languages. The proposed method consistently showed better performance in all the three languages than the baseline system, and the F-score ranged from 86.5% for British data to 95.9% for Korean drama data. Our music detection system takes about 28 s to process a 1-min signal using only one CPU with 4 cores.
关键词：Music detection; Music segmentation; Convolutional neural networks; Mel-scale filter bank