The problem, Addaquay contends, is not simply that the models fail to imitate surface sounds. They often succeed at that. The failure lies deeper, in what he calls sonic data coloniality: the processes through which musical cultures are collected, classified, abstracted, trained upon, generated and monetized within technological frameworks that remain overwhelmingly Western in design and control. The term extends the idea of data colonialism, developed by scholars Nick Couldry and Ulises Mejias, into the sonic domain. Just as human experience has been harvested as raw data for capitalist extraction, African music is being scraped, compressed and recombined into a homogenized continental sound, one that serves global markets while the communities who created the traditions remain largely absent from the pipelines, the training data decisions and the profits.

The scale of the asymmetry is striking. Recent studies have documented that generative music models almost entirely omit the Global South. A 2024 preprint titled Missing Melodies found what its authors described as a nearly complete omission of non-Western musical traditions from major text-to-music systems, and follow-up work presented at NAACL in 2025 confirmed representational bias and poor cross-cultural adaptability in leading models. A bibliometric analysis of the first twenty-five years of the International Society for Music Information Retrieval, the field’s main research community, showed that its authorship remains heavily concentrated at a Western center. Meanwhile, the commercial footprint of generative music keeps expanding: Deezer reported in April 2026 that AI-generated tracks already represent 44 percent of all newly uploaded music on its platform, meaning the biases baked into these models are now shaping the global soundscape at industrial scale.

Why do the models fail even when they try? The technical answer begins with training data. Large-scale music generation systems such as Meta’s MusicGen, Google DeepMind’s Lyria, Stability AI’s Stable Audio, Suno and ElevenLabs are trained predominantly on Western popular and classical recordings, a corpus that reflects existing licensing structures and internet availability rather than global musical diversity. African recordings, where they exist at all, are often locked in archives such as the International Library of African Music at Rhodes University, whose vast collections of field recordings have only recently been digitized. What little African music does enter training pipelines is frequently labeled under the catch-all category of world music, a classification that flattens thousands of distinct traditions, from Ewe agbadza drumming to isicathamiya choral singing to Yoruba talking-drum language, into a single undifferentiated label that the model then averages into stereotype.

The deeper technical difficulty is that many African musical systems encode knowledge that Western-derived representations struggle to capture. In many African tone languages, the pitch contour of speech carries lexical meaning, and songs frequently map melodic lines directly onto tonal patterns, so that singing the wrong melody changes the words themselves. Research on Northern Ewe music by musicologist Kofi Agawu, studies of Akan tone encoding across musical modalities, and work on the Yoruba gángan talking drum, which reproduces speech tones to communicate proverbs and names, all demonstrate that pitch, language and meaning are inseparable in these traditions. A generative model trained on spectrograms and tokenized audio has no representation of linguistic tone, no notion that a melodic interval might be a grammatical error. Similarly, West African polyrhythm and the standard bell pattern are not merely rhythmic sequences but networks of interlocking parts, microtiming relationships and dance implications that only make sense within a performance system.

Addaquay’s framework proposes a shift in how AI-generated African music should be judged. Rather than asking whether an output sounds authentic or pure, a criterion he rejects as essentialist, he argues for cultural competence: whether the system can produce music that is comprehensible within the cultural, linguistic and social systems that give the tradition meaning. Evaluation, he suggests, should examine vocality, tone-language relationships, rhythm, groove, microtiming, instrumental idiom, call-and-response structures, dance implications, social function, and the questions of consent and benefit-sharing that surround the data itself. Call-and-response in Ewe agbadza songs, for instance, is not a decorative texture but one element in a network of musical and social factors; a model that renders it as a static loop has reproduced the sound without the practice.

To make such evaluation practical, the article outlines a prompt-based and source-conditioned testing methodology. Generic prompts, such as a request for African music, reveal the model’s default stereotypes. Culturally specific prompts, naming particular traditions, instruments or social contexts, test whether the model can differentiate at all. Expert prompts, written with musicological precision, probe the limits of controllability. Source-conditioned workflows, in which users condition generation on rights-cleared African recordings, test whether the system can learn from a specific tradition rather than a continental average. Listening tests can then be structured using established subjective assessment protocols such as the ITU-R BS.1534 MUSHRA standard and web-based frameworks like WebMUSHRA, with evaluators drawn from the relevant cultural communities rather than from generic crowdsourcing pools. Live evaluation arenas for text-to-music models, now emerging in the research literature, could be extended with culturally stratified judge panels.

The governance dimension is as important as the technical one. The African Union has moved to assert continental control over the data and AI agenda, adopting a data policy framework in 2022 and a continental artificial intelligence strategy in 2024, and signing a partnership with the Music In Africa Foundation in 2025. International instruments are also converging on the issue: UNESCO’s 2021 Recommendation on the Ethics of Artificial Intelligence and its 2025 expert report on AI and culture, alongside World Intellectual Property Organization guidance on documenting traditional knowledge and traditional cultural expressions, all point toward frameworks in which communities hold rights over their cultural data. Legal pressure is mounting elsewhere too, with musicians’ unions suing record labels over AI licensing and the Swedish collecting society STIM launching the world’s first AI license for music. Whether African musicians and institutions will hold equivalent leverage over the sonic data extracted from their traditions remains an open and urgent question.

Encouragingly, African-led initiatives are building the missing infrastructure. Large-scale multilingual speech corpora such as WAXAL, developed with Google Research, demonstrate that rights-cleared, community-aware African audio datasets are feasible at scale. Organizations including Masakhane, the African Languages Lab and Khaya AI are building African language technology from within the continent, and the CompMusic project has long argued for music information research organized around specific musical cultures rather than Western defaults. Scholars of decolonial AI, including work on epistemic sovereignty in African AI governance, argue that the goal is not to bolt African content onto Western systems but to reshape the taxonomies, evaluation criteria and ownership structures themselves. Addaquay’s own prior research on sub-Saharan singing styles, traditional dance accompaniment and music preservation in Ghana supplies the musicological grounding for that reshape.

The stakes extend far beyond academic debate. If AI-generated music continues its explosive growth, the sonic image of an entire continent will increasingly be produced by models that hear Africa through Western ears, trained on data extracted without consent, evaluated by metrics that reward surface resemblance and monetized by companies far from the source communities. Addaquay’s argument is that African music should enter AI systems not as a homogenized continental sound but through its distinct traditions, hybrid practices, performance expertise and cultural authorities. Cultural competence, consent and benefit-sharing, he suggests, are not constraints on innovation but the conditions under which musical intelligence can claim to be intelligent at all. The alternative is a future in which anyone can prompt for Africa, but almost no one, including the machines, actually hears it.

Subject of Research: Cultural bias and sonic data coloniality in generative AI music systems for African musical traditions

Article Title: Prompting Africa, hearing the West: generative AI music, sonic data coloniality and the future of culturally accountable musical intelligence

Article References: Addaquay, A. P. (2026). Prompting Africa, hearing the West: generative AI music, sonic data coloniality and the future of culturally accountable musical intelligence. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03345-7

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03345-7

Keywords: generative AI music, African music, sonic data coloniality, music information retrieval, cultural competence, AI ethics, dataset bias, text-to-music models, decolonial AI, tone language, call-and-response, benefit-sharing