Artificial reverberation is an important music production tool with a strong but poorly understood perceptual impact. A literature review of the relevant works concerned with the perception of musical reverberation is provided, and the use of artificial reverberation in multisource mixes is studied. The perceived amount of total artificial reverberation in a mixture is predicted using relative reverb loudness and early decay time, as extracted from the newly proposed Equivalent Impulse Response. Results indicate that both features have a significant impact on the perception of a mix and that they are closely related to the upper and lower bounds of desired amount of reverberation in a mixture.
What audio embedding approach generalizes best to a wide range of downstream tasks across a variety of everyday domains without fine-tuning? The aim of the HEAR 2021 NeurIPS challenge is to develop a general-purpose audio representation that provides a strong basis for learning in a wide variety of tasks and scenarios. HEAR 2021 evaluates audio representations using a benchmark suite across a variety of domains, including speech, environmental sound, and music. In the spirit of shared exchange, each participant submitted an audio embedding model following a common API that is general-purpose, open-source, and freely available to use. Twenty-nine models by thirteen external teams were evaluated on nineteen diverse downstream tasks derived from sixteen datasets. Open evaluation code, submitted models and datasets are key contributions, enabling comprehensive and reproducible evaluation, as well as previously impossible longitudinal studies. It still remains an open question whether one single general-purpose audio representation can perform as holistically as the human ear.
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