论文标题
通过从积极和未标记的数据中学习的音频信号增强
Audio Signal Enhancement with Learning from Positive and Unlabelled Data
论文作者
论文摘要
监督学习是一种主流信号增强功能（SE）的主流方法，需要并行训练数据，该数据包括噪声信号和相应的清洁信号。这样的数据只能合成，并与真实数据不匹配，这可能会导致实际数据的性能差。此外，在某些情况下，清洁信号可能无法访问，这使得这种常规的方法不可行。在这里，我们使用由嘈杂信号和噪声组成的非平行训练数据探索SE，可以轻松记录。我们将正（p）和负（n）类定义为信号不活跃和活性。我们观察到，噪声夹的频谱图可以用作P数据，而噪声信号夹的频谱图可以用作未标记的数据。因此，从积极和未标记的数据中学习使卷积神经网络能够学习将每个频谱图片分类为P或N以启用SE。
Supervised learning is a mainstream approach to audio signal enhancement (SE) and requires parallel training data consisting of both noisy signals and the corresponding clean signals. Such data can only be synthesised and are mismatched with real data, which can result in poor performance on real data. Moreover, clean signals may be inaccessible in certain scenarios, which renders this conventional approach infeasible. Here we explore SE using non-parallel training data consisting of noisy signals and noise, which can be easily recorded. We define the positive (P) and the negative (N) classes as signal inactivity and activity, respectively. We observe that the spectrogram patches of noise clips can be used as P data and those of noisy signal clips as unlabelled data. Thus, learning from positive and unlabelled data enables a convolutional neural network to learn to classify each spectrogram patch as P or N to enable SE.
