  Research into the prediction and analysis of perceived audio quality is
hampered by the scarcity of openly available datasets of audio signals
accompanied by corresponding subjective quality scores. To address this
problem, we present the Open Dataset of Audio Quality (ODAQ), a new dataset
containing the results of a MUSHRA listening test conducted with expert
listeners from 2 international laboratories. ODAQ contains 240 audio samples
and corresponding quality scores. Each audio sample is rated by 26 listeners.
The audio samples are stereo audio signals sampled at 44.1 or 48 kHz and are
processed by a total of 6 method classes, each operating at different quality
levels. The processing method classes are designed to generate quality
degradations possibly encountered during audio coding and source separation,
and the quality levels for each method class span the entire quality range. The
diversity of the processing methods, the large span of quality levels, the high
sampling frequency, and the pool of international listeners make ODAQ
particularly suited for further research into subjective and objective audio
quality. The dataset is released with permissive licenses, and the software
used to conduct the listening test is also made publicly available.
