Large-Scale Weakly Supervised Audio Classification Using Gated Convolutional Neural Network
Large-Scale Weakly Supervised Audio Classification Using Gated Convolutional Neural Network
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DOI：
10.1109/icassp.2018.8461975
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发表时间：
2017-10
期刊：
影响因子：
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通讯作者：
Yong Xu;Qiuqiang Kong;Wenwu Wang;Mark D. Plumbley
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文献类型：
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作者：
Yong Xu;Qiuqiang Kong;Wenwu Wang;Mark D. Plumbley
在本文中，我们提出了一种门控卷积神经网络和一种基于时间注意力的音频分类定位方法，该方法在声学场景和事件检测与分类（DCASE）2017 年挑战赛的大规模弱监督声音事件检测任务中获得第一名。此任务中的音频片段是从 YouTube 视频中提取的，手动标记有一个或多个音频标签，但没有音频事件的时间戳，因此称为弱标记数据。此挑战定义了两个子任务，包括音频标记和使用弱标记数据的声音事件检测。我们提出了一种卷积循环神经网络（CRNN），其具有应用于对数梅尔频谱图的可学习门控线性单元（GLU）非线性。此外，我们提出了一种沿帧的时间注意力方法，以根据弱标记数据预测块中每个音频事件的位置。我们的系统在 DCASE 2017 挑战赛的这两个子任务中作为团队的表现分别排名第一和第二，F 值 55.6%，等误差 0.73。
In this paper, we present a gated convolutional neural network and a temporal attention-based localization method for audio classification, which won the 1st place in the large-scale weakly supervised sound event detection task of Detection and Classification of Acoustic Scenes and Events (DCASE) 2017 challenge. The audio clips in this task, which are extracted from YouTube videos, are manually labelled with one or more audio tags, but without time stamps of the audio events, hence referred to as weakly labelled data. Two subtasks are defined in this challenge including audio tagging and sound event detection using this weakly labelled data. We propose a convolutional recurrent neural network (CRNN) with learnable gated linear units (GLUs) non-linearity applied on the log Mel spectrogram. In addition, we propose a temporal attention method along the frames to predict the locations of each audio event in a chunk from the weakly labelled data. The performances of our systems were ranked the 1st and the 2nd as a team in these two sub-tasks of DCASE 2017 challenge with F value 55.6% and Equal error 0.73, respectively.