  Data sonification-mapping data variables to auditory variables, such as pitch
or volume-is used for data accessibility, scientific exploration, and
data-driven art (e.g., museum exhibitions) among others. While a substantial
amount of research has been made on effective and intuitive sonification
design, software support is not commensurate, limiting researchers from fully
exploring its capabilities. We contribute Erie, a declarative grammar for data
sonification, that enables abstractly expressing auditory mappings. Erie
supports specifying extensible tone designs (e.g., periodic wave, sampling,
frequency/amplitude modulation synthesizers), various encoding channels,
auditory legends, and composition options like sequencing and overlaying. Using
standard Web Audio and Web Speech APIs, we provide an Erie compiler for web
environments. We demonstrate the expressiveness and feasibility of Erie by
replicating research prototypes presented by prior work and provide a
sonification design gallery. We discuss future steps to extend Erie toward
other audio computing environments and support interactive data sonification.
