Deep learning algorithms are rapidly changing the way in which audiovisual media can be produced. Synthetic audiovisual media generated with deep learning—often subsumed colloquially under the label “deepfakes”—have a number of impressive characteristics; they are increasingly trivial to produce, and can be indistinguishable from real sounds and images recorded with a sensor. Much attention has been dedicated to ethical concerns raised by this technological development. Here, I focus instead on a set of issues related to the notion of synthetic audiovisual (...) media, its place within a broader taxonomy of audiovisual media, and how deep learning techniques differ from more traditional approaches to media synthesis. After reviewing important etiological features of deep learning pipelines for media manipulation and generation, I argue that “deepfakes” and related synthetic media produced with such pipelines do not merely offer incremental improvements over previous methods, but challenge traditional taxonomical distinctions, and pave the way for genuinely novel kinds of audiovisual media. (shrink)

Along with pitch and loudness, timbre is commonly described as an audible property of sounds. This paper puts forward an alternative view—that timbres are properties of auditory media. This approach has many advantages. First, it accounts for the frequent attribution of timbres to objects that do not have characteristic sounds. Second, it explains why timbres are attributed not only to ordinary objects, like musical instruments, but also to surrounding spaces and architectural structures. And finally, it provides an original solution to (...) the timbre-constancy problem. (shrink)