Synthetic creation of drum sounds (e.g., in drum machines)is commonly performed using analog or digital synthesis,allowing a musician to sculpt the desired timbre modify-ing various parameters. Typically, such parameters controllow-level features of the sound and often have no musicalmeaning or perceptual correspondence. With the rise ofDeep Learning, data-driven processing of audio emergesas an alternative to traditional signal processing. This newparadigm allows controlling the synthesis process throughlearned high-level features or by conditioning a modelon musically relevant information. In this paper, we ap-ply a Generative Adversarial Network to the task of au-dio synthesis of drum sounds. By conditioning the modelon perceptual features computed with a publicly availablefeature-extractor, intuitive control is gained over the gen-eration process. The experiments are carried out on a largecollection of kick, snare, and cymbal sounds. We showthat, compared to a specific prior work based on a U-Netarchitecture, our approach considerably improves the qual-ity of the generated drum samples, and that the conditionalinput indeed shapes the perceptual characteristics of thesounds. Also, we provide audio examples and release thecode for reproducibility.1