  Searching sounds by text labels is often difficult, as text descriptions
cannot describe the audio content in detail. Query by vocal imitation bridges
such gap and provides a novel way to sound search. Several algorithms for sound
search by vocal imitation have been proposed and evaluated in a simulation
environment, however, they have not been deployed into a real search engine nor
evaluated by real users. This pilot work conducts a subjective study to compare
these two approaches to sound search, and tries to answer the question of which
approach works better for what kinds of sounds. To do so, we developed two
web-based search engines for sound, one by vocal imitation (Vroom!) and the
other by text description (TextSearch). We also developed an experimental
framework to host these engines to collect statistics of user behaviors and
ratings. Results showed that Vroom! received significantly higher search
satisfaction ratings than TextSearch did for sound categories that were
difficult for subjects to describe by text. Results also showed a better
overall ease-of-use rating for Vroom! than TextSearch on the limited sound
library in our experiments. These findings suggest advantages of
vocal-imitation-based search for sound in practice.
