  In movie productions, the Foley Artist is responsible for creating an overlay
soundtrack that helps the movie come alive for the audience. This requires the
artist to first identify the sounds that will enhance the experience for the
listener thereby reinforcing the Directors's intention for a given scene. In
this paper, we present AutoFoley, a fully-automated deep learning tool that can
be used to synthesize a representative audio track for videos. AutoFoley can be
used in the applications where there is either no corresponding audio file
associated with the video or in cases where there is a need to identify
critical scenarios and provide a synthesized, reinforced soundtrack. An
important performance criterion of the synthesized soundtrack is to be
time-synchronized with the input video, which provides for a realistic and
believable portrayal of the synthesized sound. Unlike existing sound prediction
and generation architectures, our algorithm is capable of precise recognition
of actions as well as inter-frame relations in fast moving video clips by
incorporating an interpolation technique and Temporal Relationship Networks
(TRN). We employ a robust multi-scale Recurrent Neural Network (RNN) associated
with a Convolutional Neural Network (CNN) for a better understanding of the
intricate input-to-output associations over time. To evaluate AutoFoley, we
create and introduce a large scale audio-video dataset containing a variety of
sounds frequently used as Foley effects in movies. Our experiments show that
the synthesized sounds are realistically portrayed with accurate temporal
synchronization of the associated visual inputs. Human qualitative testing of
AutoFoley show over 73% of the test subjects considered the generated
soundtrack as original, which is a noteworthy improvement in cross-modal
research in sound synthesis.
