GANs have been used extensively for Image Synthesis, Image to Image Translation, and many other image tasks. More recently, they have been applied to the task of raw audio synthesis by Donahue et al with their WaveGAN architecture. Previous audio generation techniques relied on HMMs, autoregressive models, or applying image-based techniques to spectrograms (images of waveforms in the time-domain). Donahue et al demonstrated that by applying a 1D version of DCGAN directly to regular (normalized) audio files, one could generate high quality samples of human speech superior to these older techniques. The papers's goal was to generate speech, but the authors also applied their GAN to a small dataset of drum hits and were able to produce high quality samples. This project aims to explore the ability of the same model architecture to generate significantly more complex audio patterns, namely drum beats. In particular, using the architecture presented in the paper, a GAN is trained to generate the first bar in a 4 bar drum pattern. The model is able to produce high quality samples close to par with those published in the original paper. Furthermore, the majority of the GAN's outputs are new beats, rather than re-hashes of the training data, as measured by a quantitative similarity metric and user testing. WaveGAN's generator outputs vectors of shape (16384, c), where c is the number of audio channels. This allows the number of params in WaveGAN to be the same as in DCGAN. A larger model would require more parameters, thus significantly more data.