Towards the Synthesis of Non-speech Vocalizations
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929538597912576 |
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| author | Hoq, Enjamamul Nwogu, Ifeoma |
| author_facet | Hoq, Enjamamul Nwogu, Ifeoma |
| contents | In this report, we focus on the unconditional generation of infant cry sounds using the DiffWave framework, which has shown great promise in generating high-quality audio from noise. We use two distinct datasets of infant cries: the Baby Chillanto and the deBarbaro cry dataset. These datasets are used to train the DiffWave model to generate new cry sounds that maintain high fidelity and diversity. The focus here is on DiffWave's capability to handle the unconditional generation task. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_09360 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards the Synthesis of Non-speech Vocalizations Hoq, Enjamamul Nwogu, Ifeoma Sound Machine Learning Audio and Speech Processing In this report, we focus on the unconditional generation of infant cry sounds using the DiffWave framework, which has shown great promise in generating high-quality audio from noise. We use two distinct datasets of infant cries: the Baby Chillanto and the deBarbaro cry dataset. These datasets are used to train the DiffWave model to generate new cry sounds that maintain high fidelity and diversity. The focus here is on DiffWave's capability to handle the unconditional generation task. |
| title | Towards the Synthesis of Non-speech Vocalizations |
| topic | Sound Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2410.09360 |