in Github[^3] to inpaint the masked spectrogram. We change the target iteration from 6001 to 4001 to reduce the processing time while still maintaining the quality of the audio.

### GMCNN

is one of the state-of-the-art image inpainting framework that features a multi-column neural network that could model different image components and extract multi-level features to aid inpainting.

We experiment with the framework provided in Github [^4] and modify the model to take a masked spectrogram as input instead of a 256 \* 256 image with RGB channels. Since the spectrogram is one channel, we firstly modify the pretrained model by changing the first layer of the generator to a conv layer with one input channel, the last decoding layer to a conv layer with one output channel and the first layer of both the global and local discriminator to a conv layer with one input channel. These conv layers are all initialized randomly with normal distribution. We then finetune the model for 40 epochs using the default settings.

### SimilarityGraph

is an example-based framework that targets particularly at long gaps in music. It detects spectro-temporal similarities among unmasked data to the masked area, solving case when adjacent segments fail 