AES Show 2024 NY

Datasets of processed audio signals along with subjective quality scores are instrumental for research into perception-based audio processing algorithms and objective audio quality metrics. However, openly available datasets are scarce due to listening test effort and copyright concerns limiting the distribution of audio material in existing datasets. To address this problem, Open Dataset of Audio Quality (ODAQ) was introduced, containing audio material along with extensive subjective test results with permissive licenses. The dataset comprises processed audio material with six different classes of signal impairments in multiple levels of processing strength covering a wide range of quality levels. The subjective quality evaluation has recently been extended and now comprises results from three international laboratories providing a total 42 listeners and 10080 subjective scores overall. Furthermore, ODAQ was recently expanded by a performance evaluation of common objective metrics for perceptual quality evaluation in their ability to predict subjective scores. The wide variety of audio material and test subjects in the test provides insight into influences and biases in subjective evaluation, which we investigated by statistical analysis, finding listener-based, training-based and lab-based influences. We also demonstrate the methodology for contributing to ODAQ, and make a request for additional contributors. In conclusion, the diversity of the processing methods and quality levels, along with a large pool of international listeners and permissive licenses make ODAQ particularly suited for further research into subjective and objective audio quality.