Singing Voice Detection Using Multi-Feature Deep Fusion with CNN
Singing Voice Detection Using Multi-Feature Deep Fusion with CNN
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DOI：
10.1007/978-981-15-2756-2_4
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Xulong Zhang;Shengchen Li;Zijin Li;Shizhe Chen;Yongwei Gao;Wei Li-
中科院分区：
文献类型：
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作者：
Xulong Zhang;Shengchen Li;Zijin Li;Shizhe Chen;Yongwei Gao;Wei Li-
歌声检测的问题是将歌曲分割成有声部分和非有声部分。通常使用的方法通常在一组基于帧的特征上训练模型，然后通过模型预测未知帧。然而，多维特征通常对于每个帧连接在一起，很少考虑空间信息。为此，提出了一种基于卷积神经网络（CNN）的多特征维度深度融合方法。对每帧图像的特征维数进行一维卷积，得到的高层特征可用于直接的二值分类。该方法的性能与公共数据集上的最新方法相当。
The problem of singing voice detection is to segment a song into vocal and non-vocal parts. Commonly used methods usually train a model on a set of frame-based features and then predict the unknown frames by the model. However, the multi-dimensional features are usually concatenated together for each frame, with little consideration of spatial information. Hence, a deep fusion method of the Multi-feature dimensions with Convolution Neural Networks (CNN) is proposed. A one dimension convolution is made on feature dimensions for each frames, then the high-level features obtained can be used for a direct binary classification. The performance of the proposed method is on par with the state-of-art methods on public dataset.