On 28th of March, our partner Orfium took part in the 4th Workshop on Natural Language Processing for Music and Audio (NLP4MusA 2026). This article provides an overview of their participation and highlights the relevance of their contribution to responsible AI in the music domain.
This year’s workshop focused on exploring the multimodal synergies between language, music, and sound, bringing together research at the intersection of NLP and creative media, with particular relevance to the entertainment industry.
How can we make AI systems more transparent, fair, and truly trustworthy?
During the workshop, Orfium presented LabelBuddy, an open-source, AI-assisted annotation and tagging tool designed to help researchers efficiently label music- and audio-related text data. LabelBuddy leverages intelligent AI suggestions to support the creation of high-quality datasets for applications in natural language processing, music information retrieval, and multimodal AI. By combining automation with human validation, the tool accelerates dataset development while ensuring quality control and human oversight.
This initiative closely aligns with AIXPERT’s mission to advance human-centric, explainable, and trustworthy AI. Tools such as LabelBuddy reflect key principles of Explainable AI (XAI), transparency, accountability, fairness, and responsible governance. By embedding human feedback into AI-driven workflows, they support robust and ethically grounded AI systems: core objectives of the AIXPERT platform.
From an industry perspective, LabelBuddy is particularly relevant to Orfium’s work in the music ecosystem. As a technology company operating at the intersection of music, data, and innovation, Orfium relies on accurate, scalable data processing to support applications such as music understanding, content analysis, and rights management. AI-assisted annotation enables more efficient structuring of music-related data, which is essential for building advanced, reliable, and responsible AI solutions in the creative industries.
Through this participation, Orfium demonstrates how research-driven tools and trustworthy AI principles can directly contribute to innovation in the music sector, bridging academia, industry, and responsible AI development.