Content exchange and collaboration serve as catalysts for repository creation that supports creative industries and fuels model development in machine learning and AI. Despite numerous repositories, challenges persist in discoverability, rights preservation, and efficient reuse of audiovisual assets. To address these issues, the SCENE (Searchable multi-dimensional Data Lakes supporting Cognitive Film Production & Distribution for the Promotion of the European Cultural Heritage) project introduces an automated audio quality assessment toolkit integrated within its Media Assets Management (MAM) platform. This toolkit comprises a suite of advanced metrics, such as artifact detection, bandwidth estimation, compression history analysis, noise profiling, speech intelligibility, environmental sound recognition, and reverberation characterization. The metrics are extracted using dedicated Flask-based web services that interface with a data lake architecture. By streamlining the inspection of large-scale audio repositories, the proposed solution benefits both high-end film productions and smaller-scale collaborations. The pilot phase of the toolkit will involve professional filmmakers who will provide feedback to refine post-production workflows. This paper presents the motivation, design, and implementation details of the toolkit, highlighting its potential to assess content quality management and contribute to more efficient content exchange in the creative industries.
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https://aes2.org/publications/elibrary-page/?id=22890
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