  In this paper we propose modifications to the neural network framework,
AutoVC for the task of singing technique conversion. This includes utilising a
pretrained singing technique encoder which extracts technique information, upon
which a decoder is conditioned during training. By swapping out a source
singer's technique information for that of the target's during conversion, the
input spectrogram is reconstructed with the target's technique. We document the
beneficial effects of omitting the latent loss, the importance of sequential
training, and our process for fine-tuning the bottleneck. We also conducted a
listening study where participants rate the specificity of technique-converted
voices as well as their naturalness. From this we are able to conclude how
effective the technique conversions are and how different conditions affect
them, while assessing the model's ability to reconstruct its input data.
