@inproceedings{al-ghawanmeh-etal-2023-machine,
title = "How can machine translation help generate {A}rab melodic improvisation?",
author = "Al-Ghawanmeh, Fadi and
Jensenius, Alexander and
Smaili, Kamel",
editor = "Nurminen, Mary and
Brenner, Judith and
Koponen, Maarit and
Latomaa, Sirkku and
Mikhailov, Mikhail and
Schierl, Frederike and
Ranasinghe, Tharindu and
Vanmassenhove, Eva and
Vidal, Sergi Alvarez and
Aranberri, Nora and
Nunziatini, Mara and
Escart{\'\i}n, Carla Parra and
Forcada, Mikel and
Popovic, Maja and
Scarton, Carolina and
Moniz, Helena",
booktitle = "Proceedings of the 24th Annual Conference of the European Association for Machine Translation",
month = jun,
year = "2023",
address = "Tampere, Finland",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2023.eamt-1.38",
pages = "385--392",
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 maq{\=a}m (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 maq{\=a}m music tradition were promising, and a useful reference for understanding objective results.e translations of the most common patterns of each maq{\=a}m (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 maq{\=a}m music tradition were promising, and a useful reference for understanding objective results.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="al-ghawanmeh-etal-2023-machine">
<titleInfo>
<title>How can machine translation help generate Arab melodic improvisation?</title>
</titleInfo>
<name type="personal">
<namePart type="given">Fadi</namePart>
<namePart type="family">Al-Ghawanmeh</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alexander</namePart>
<namePart type="family">Jensenius</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kamel</namePart>
<namePart type="family">Smaili</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2023-06</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 24th Annual Conference of the European Association for Machine Translation</title>
</titleInfo>
<name type="personal">
<namePart type="given">Mary</namePart>
<namePart type="family">Nurminen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Judith</namePart>
<namePart type="family">Brenner</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Maarit</namePart>
<namePart type="family">Koponen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sirkku</namePart>
<namePart type="family">Latomaa</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mikhail</namePart>
<namePart type="family">Mikhailov</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Frederike</namePart>
<namePart type="family">Schierl</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Tharindu</namePart>
<namePart type="family">Ranasinghe</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Eva</namePart>
<namePart type="family">Vanmassenhove</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sergi</namePart>
<namePart type="given">Alvarez</namePart>
<namePart type="family">Vidal</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nora</namePart>
<namePart type="family">Aranberri</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mara</namePart>
<namePart type="family">Nunziatini</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Carla</namePart>
<namePart type="given">Parra</namePart>
<namePart type="family">Escartín</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mikel</namePart>
<namePart type="family">Forcada</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Maja</namePart>
<namePart type="family">Popovic</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Carolina</namePart>
<namePart type="family">Scarton</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Helena</namePart>
<namePart type="family">Moniz</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>European Association for Machine Translation</publisher>
<place>
<placeTerm type="text">Tampere, Finland</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<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.</abstract>
<identifier type="citekey">al-ghawanmeh-etal-2023-machine</identifier>
<location>
<url>https://aclanthology.org/2023.eamt-1.38</url>
</location>
<part>
<date>2023-06</date>
<extent unit="page">
<start>385</start>
<end>392</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T How can machine translation help generate Arab melodic improvisation?
%A Al-Ghawanmeh, Fadi
%A Jensenius, Alexander
%A Smaili, Kamel
%Y Nurminen, Mary
%Y Brenner, Judith
%Y Koponen, Maarit
%Y Latomaa, Sirkku
%Y Mikhailov, Mikhail
%Y Schierl, Frederike
%Y Ranasinghe, Tharindu
%Y Vanmassenhove, Eva
%Y Vidal, Sergi Alvarez
%Y Aranberri, Nora
%Y Nunziatini, Mara
%Y Escartín, Carla Parra
%Y Forcada, Mikel
%Y Popovic, Maja
%Y Scarton, Carolina
%Y Moniz, Helena
%S Proceedings of the 24th Annual Conference of the European Association for Machine Translation
%D 2023
%8 June
%I European Association for Machine Translation
%C Tampere, Finland
%F al-ghawanmeh-etal-2023-machine
%X 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.
%U https://aclanthology.org/2023.eamt-1.38
%P 385-392
Markdown (Informal)
[How can machine translation help generate Arab melodic improvisation?](https://aclanthology.org/2023.eamt-1.38) (Al-Ghawanmeh et al., EAMT 2023)
ACL