We demonstrate a conditional autoregressive pipeline for efficient music recomposition, based on methods presented in van den (2017). Recomposition (Casal & Casey, 2010) focuses on reworking existing musical pieces, adhering to structure at a high level while also re-imagining other aspects of the work. This can involve reuse of pre-existing themes or parts of the original piece, while also requiring the flexibility to generate new content at different levels of granularity. Applying the aforementioned modeling pipeline to recomposition, we show diverse and structured generation conditioned on chord sequence annotations.
Autoregressive models have proven to be powerful distribution estimators for images and sequence data, showing excellent results in generative settings . They have also performed well in related prior work for polyphonic music generation @cite_0 . Most related to the work described in this paper is CoCoNet , which also uses an autoregressive convolutional model over image-like structures for polyphonic music generation and was a direct inspiration for our approach. One key difference of our approach is our utilization of a two stage pipeline (first seen in the work of ) which greatly improves training and generation speed as well as creating an implicit separation between local voice agreement (first stage) and global consistency over measures (second stage).
@cite_0
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Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers---8x deeper than VGG nets but still having lower complexity. An ensemble of these residual nets achieves 3.57 error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28 relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.