Learning Relationships Between Separate Audio Tracks for Creative Applications
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915523023863808 |
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| author | Bujard, Balthazar Nika, Jérôme Bevilacqua, Fédéric Obin, Nicolas |
| author_facet | Bujard, Balthazar Nika, Jérôme Bevilacqua, Fédéric Obin, Nicolas |
| contents | This paper presents the first step in a research project situated within the field of musical agents. The objective is to achieve, through training, the tuning of the desired musical relationship between a live musical input and a real-time generated musical output, through the curation of a database of separated tracks. We propose an architecture integrating a symbolic decision module capable of learning and exploiting musical relationships from such musical corpus. We detail an offline implementation of this architecture employing Transformers as the decision module, associated with a perception module based on Wav2Vec 2.0, and concatenative synthesis as audio renderer. We present a quantitative evaluation of the decision module's ability to reproduce learned relationships extracted during training. We demonstrate that our decision module can predict a coherent track B when conditioned by its corresponding ''guide'' track A, based on a corpus of paired tracks (A, B). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25296 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Relationships Between Separate Audio Tracks for Creative Applications Bujard, Balthazar Nika, Jérôme Bevilacqua, Fédéric Obin, Nicolas Sound Artificial Intelligence Human-Computer Interaction Machine Learning Audio and Speech Processing This paper presents the first step in a research project situated within the field of musical agents. The objective is to achieve, through training, the tuning of the desired musical relationship between a live musical input and a real-time generated musical output, through the curation of a database of separated tracks. We propose an architecture integrating a symbolic decision module capable of learning and exploiting musical relationships from such musical corpus. We detail an offline implementation of this architecture employing Transformers as the decision module, associated with a perception module based on Wav2Vec 2.0, and concatenative synthesis as audio renderer. We present a quantitative evaluation of the decision module's ability to reproduce learned relationships extracted during training. We demonstrate that our decision module can predict a coherent track B when conditioned by its corresponding ''guide'' track A, based on a corpus of paired tracks (A, B). |
| title | Learning Relationships Between Separate Audio Tracks for Creative Applications |
| topic | Sound Artificial Intelligence Human-Computer Interaction Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2509.25296 |