论文标题
学会denoise历史音乐
Learning to Denoise Historical Music
论文作者
论文摘要
我们提出了一个录音录音录音的录音录音。我们的模型通过短时傅立叶变换（STFT）将其输入转换为时频表示，并使用卷积神经网络处理所得的复杂频谱图。该网络在合成嘈杂的音乐数据集中培训了重建和对抗性目标，该数据集是通过将干净的音乐与从旧录音的安静片段中提取的真实噪声样本混合而创建的。我们在合成数据集的持有测试示例上定量评估我们的方法，并通过人类对实际历史记录的样本进行定性评级。我们的结果表明，所提出的方法可有效消除噪音，同时保留原始音乐的质量和细节。
We propose an audio-to-audio neural network model that learns to denoise old music recordings. Our model internally converts its input into a time-frequency representation by means of a short-time Fourier transform (STFT), and processes the resulting complex spectrogram using a convolutional neural network. The network is trained with both reconstruction and adversarial objectives on a synthetic noisy music dataset, which is created by mixing clean music with real noise samples extracted from quiet segments of old recordings. We evaluate our method quantitatively on held-out test examples of the synthetic dataset, and qualitatively by human rating on samples of actual historical recordings. Our results show that the proposed method is effective in removing noise, while preserving the quality and details of the original music.
