Abstract
This article presents a system to generate Arab music improvisation using machine translation. To reach this goal, we developed a machine translation model to translate a vocal improvisation into an automatic instrumental oud (Arab lute) response. Given the melodic and non-metric musical form, it was necessary to develop efficient textual representations for classical machine translation models to be as successful as NLP applications. We experimented with SMT and NMT to train our parallel corpus (Vocal to Instrumental) of 6991 sentences. The best model was then used to generate improvisation by iteratively translating thThis article presents a system to generate Arab music improvisation using machine translation (MT). To reach this goal, we developed a MT model to translate a vocal improvisation into an automatic instrumental oud (Arab lute) response. Given the melodic and non-metric musical form, it was necessary to develop efficient textual representations in order for classical MT models to be as successful as in common NLP applications. We experimented with Statistical and Neural MT to train our parallel corpus (Vocal to Instrument) of 6991 sentences. The best model was then used to generate improvisation by iteratively translating the translations of the most common patterns of each maqām (n-grams), producing elaborated variations conditioned to listener feedback. We constructed a dataset of 717 instrumental improvisations to extract their n-grams. Objective evaluation of MT was conducted at two levels: a sentence-level evaluation using the BLEU metric, and a higher level evaluation using musically informed metrics. Objective measures were consistent with one another. Subjective evaluations by experts from the maqām music tradition were promising, and a useful reference for understanding objective results.e translations of the most common patterns of each maqām (n-grams), producing elaborated variations conditioned to listener feedback. We constructed a dataset of 717 instrumental improvisations to extract their n-grams. Objective evaluation of machine translation was conducted at two levels: a sentence-level evaluation using the BLEU metric, and a higher level evaluation using musically informed metrics. Objective measures were consistent with one another. Subjective evaluations by experts from the maqām music tradition were promising, and a useful reference for understanding objective results.- Anthology ID:
- 2023.eamt-1.38
- Volume:
- Proceedings of the 24th Annual Conference of the European Association for Machine Translation
- Month:
- June
- Year:
- 2023
- Address:
- Tampere, Finland
- Editors:
- Mary Nurminen, Judith Brenner, Maarit Koponen, Sirkku Latomaa, Mikhail Mikhailov, Frederike Schierl, Tharindu Ranasinghe, Eva Vanmassenhove, Sergi Alvarez Vidal, Nora Aranberri, Mara Nunziatini, Carla Parra Escartín, Mikel Forcada, Maja Popovic, Carolina Scarton, Helena Moniz
- Venue:
- EAMT
- SIG:
- Publisher:
- European Association for Machine Translation
- Note:
- Pages:
- 385–392
- Language:
- URL:
- https://aclanthology.org/2023.eamt-1.38
- DOI:
- Cite (ACL):
- Fadi Al-Ghawanmeh, Alexander Jensenius, and Kamel Smaili. 2023. How can machine translation help generate Arab melodic improvisation?. In Proceedings of the 24th Annual Conference of the European Association for Machine Translation, pages 385–392, Tampere, Finland. European Association for Machine Translation.
- Cite (Informal):
- How can machine translation help generate Arab melodic improvisation? (Al-Ghawanmeh et al., EAMT 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2023.eamt-1.38.pdf