Semantic mediation in audio and music intelligence through natural language processing a systematic literature review

This is the authors' abstract. We don't add key points for this paper.

This study presents a systematic literature review examining the evolving role of Natural Language Processing (NLP) in audio and music artificial intelligence between 2021 and 2025. While early research primarily treated language as an auxiliary component supporting signal-based modelling, recent developments indicate a fundamental conceptual shift in which language functions as a central mechanism for meaning construction, mediation, and explanation across modalities. Following the PRISMA framework, this review analyses 139 peer-reviewed journal articles to identify how linguistic functions are formalized as computational tasks in speech, audio, and music systems. The findings reveal a progressive transition from acoustic-centric approaches toward language-driven representations that account for semantic, pragmatic, and contextual dimensions of meaning. In speech processing, utterances are increasingly modelled as linguistic and cognitive structures rather than mere transcriptions. In music-related research, lyrics and linguistic descriptions emerge as primary carriers of musical meaning, enabling genre interpretation, affect modelling, and language-based music control. At the intersection of NLP, audio, and music, language operates as a semantic mediator aligning acoustic representations with musical structures. Beyond mapping this evolution, the review identifies persistent methodological and conceptual challenges related to non-literal meaning, cultural variability, evaluation inconsistency, dataset bias, and interpretability. Overall, this review argues for positioning language as a foundational axis for future Audio and Music AI research and contributes to a structured basis for more semantically grounded, context-aware, and explainable systems.