  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.
