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首页> 外文会议>IEEE International Conference on Acoustics, Speech and Signal Processing >An Analysis of Noise-aware Features in Combination with the Size and Diversity of Training Data for DNN-based Speech Enhancement
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An Analysis of Noise-aware Features in Combination with the Size and Diversity of Training Data for DNN-based Speech Enhancement

机译：基于DNN的语音增强与训练数据的大小和多样性相结合的噪声感知功能分析

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摘要

In this work, the generalization of speech enhancement algorithms based on deep neural networks (DNNs) for training datasets that differ in size and diversity is analyzed. For this, we compare noise aware training (NAT) features and signal-to-noise ratio (SNR) based noise aware training (SNR-NAT) features. NAT appends an estimate of the noise power spectral density (PSD) to a noisy periodogram input feature, whereas SNR-NAT uses the noise PSD for normalization. We show that the Hu noise corpus (limited size) and the CHiME 3 noise corpus (limited diversity) may result in DNNs which do not generalize well to unseen noises. We construct a large and diverse dataset from freely available data and show that it helps DNNs to generalize. However, we also show that with SNR-NAT features, the trained models are more robust even if a small or less diverse training set is employed. Using t-distributed stochastic neighbor embedding (t-SNE), we demonstrate that using SNR-NAT both the features and the resulting internal representation of the DNN are less dependent on the background noise which facilitates the generalization to unseen noise types.
机译：在这项工作中，分析了针对深度和多样性不同的训练数据集的基于深度神经网络（DNN）的语音增强算法的泛化。为此，我们比较了噪声感知训练（NAT）功能和基于信噪比（SNR）的噪声感知训练（SNR-NAT）功能。 NAT将噪声功率谱密度（PSD）的估计值附加到有噪声的周期图输入功能中，而SNR-NAT使用噪声PSD进行归一化。我们表明，胡噪声语料库（有限大小）和CHiME 3噪声语料库（有限分集）可能会导致DNN不能很好地推广到看不见的噪声。我们从免费数据中构建了一个庞大而多样的数据集，并表明它有助于DNN进行概括。但是，我们还表明，即使使用了少量或较少多样性的训练集，经过训练的模型也具有SNR-NAT功能，因此更加健壮。使用t分布随机邻居嵌入（t-SNE），我们证明了使用SNR-NAT时，DNN的特征和所得内部表示都较少依赖于背景噪声，这有利于泛化为看不见的噪声类型。

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