Synthesizing audio-reactive videos to accompany music is challenging multi-domain task that requires both a visual synthesis skill-set and an understanding of musical information extraction. In recent years a new flexible class of visual synthesis methods has gained popularity: generative adversarial networks. These deep neural networks can be trained to reproduce arbitrary images based on a dataset of about 10000 examples. After training, they can be harnessed to synthesize audio-reactive videos by constructing sequences of inputs based on musical information.

Current approaches suffer from a few problems which hamper the quality and usability of GAN-based audio-reactive video synthesis. Some approaches consider only a small number of possible musical inputs and ways of mapping these the GAN's parameters. This leads to weak audio-reactivity which has a similar motion characteristic across all musical inputs. Other approaches do harness the full design space, but are difficult to configure correctly for effective results.

This thesis aims to address the tradeoff between audio-reactive flexibility and ease of attaining effective results. We introduce multiple algorithms that explore the design space by using machine learning to generate sequences of inputs for the GAN.

To develop these machine learning algorithms, we first introduce a metric, the audiovisual correlation, that measures the audio-reactivity in a video. We use this metric to train models based only on a dataset of audio examples, avoiding the need of a large dataset of example audio-reactive videos. This self-supervised approach can even be extended to optimize a single audio-reactive video directly, removing the need to even train a model beforehand.

Our evaluation of the methods shows that our algorithms out-perform prior work in terms of their audio-reactivity. Our solutions explore a wider range of the audio-reactive space and do so without the need for manual feature extraction or configuration.

Deep neural networks have revolutionized multiple fields within computer science. It is important to have a comprehensive understanding of the memory requirements and performance of deep networks on low-resource systems. While there have been efforts to this end, the effects of severe memory limits and heavy swapping are understudied. We have profiled multiple deep networks under varying memory restrictions and on different hardware. Using this data, we develop two modeling approaches to predict the execution time of a network based on a description of its layers and the available memory. The first modeling approach is based on engineering predictive features through a theoretical analysis of the computations required to execute a layer. The second approach uses a LASSO regression to select predictive features from an expanded set of predictors. Both approaches achieve a mean absolute percentage error of 5% on log-transformed data, but suffer degraded performance on transformation of predictions back to regular space.