Sound Recognition: A Cognitive Way

Sound recognition systems aim to determine what source produced a sound event. Until now, such systems lack explicit knowledge about sound sources; they rely on crude signal descriptions and large annotated training databases. These databases, hopefully for such systems, allow a reliable correlation between signal descriptions and annotations. Some modern perception approaches are based on the concept of gist, which suggest that a signal is first analyzed crudely, not unlike conventional sound recognition systems, but that the crude analysis is followed by a more detailed knowledge driven approach. In this work I explore a way of involving explicit knowledge about sound sources and audition, resulting in a more cognitive approach to sound recognition. I find that this approach is complementary to conventional sound recognition systems, as it leads to higher classification performance on a single sound event recognition task and it gives more insight into recognitions. For example, in addition to answering what source produced a sound event, my approach can also precisely tell where (in time and frequency the evidence stems from) and why the sound event was recognized as being such. Also, this approach is extensible, as knowledge can be added and specialized, possibly increasing its performance beyond that of conventional sound recognition systems.