  We introduce the task of acoustic question answering (AQA) in the area of
acoustic reasoning. In this task an agent learns to answer questions on the
basis of acoustic context. In order to promote research in this area, we
propose a data generation paradigm adapted from CLEVR (Johnson et al. 2017). We
generate acoustic scenes by leveraging a bank elementary sounds. We also
provide a number of functional programs that can be used to compose questions
and answers that exploit the relationships between the attributes of the
elementary sounds in each scene. We provide AQA datasets of various sizes as
well as the data generation code. As a preliminary experiment to validate our
data, we report the accuracy of current state of the art visual question
answering models when they are applied to the AQA task without modifications.
Although there is a plethora of question answering tasks based on text, image
or video data, to our knowledge, we are the first to propose answering
questions directly on audio streams. We hope this contribution will facilitate
the development of research in the area.
