Towards Controllable Audio Texture Morphing
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866917794404106240 |
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| author | Gupta, Chitralekha Kamath, Purnima Wei, Yize Li, Zhuoyao Nanayakkara, Suranga Wyse, Lonce |
| author_facet | Gupta, Chitralekha Kamath, Purnima Wei, Yize Li, Zhuoyao Nanayakkara, Suranga Wyse, Lonce |
| contents | In this paper, we propose a data-driven approach to train a Generative Adversarial Network (GAN) conditioned on "soft-labels" distilled from the penultimate layer of an audio classifier trained on a target set of audio texture classes. We demonstrate that interpolation between such conditions or control vectors provides smooth morphing between the generated audio textures, and shows similar or better audio texture morphing capability compared to the state-of-the-art methods. The proposed approach results in a well-organized latent space that generates novel audio outputs while remaining consistent with the semantics of the conditioning parameters. This is a step towards a general data-driven approach to designing generative audio models with customized controls capable of traversing out-of-distribution regions for novel sound synthesis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_11648 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Towards Controllable Audio Texture Morphing Gupta, Chitralekha Kamath, Purnima Wei, Yize Li, Zhuoyao Nanayakkara, Suranga Wyse, Lonce Audio and Speech Processing Artificial Intelligence Sound In this paper, we propose a data-driven approach to train a Generative Adversarial Network (GAN) conditioned on "soft-labels" distilled from the penultimate layer of an audio classifier trained on a target set of audio texture classes. We demonstrate that interpolation between such conditions or control vectors provides smooth morphing between the generated audio textures, and shows similar or better audio texture morphing capability compared to the state-of-the-art methods. The proposed approach results in a well-organized latent space that generates novel audio outputs while remaining consistent with the semantics of the conditioning parameters. This is a step towards a general data-driven approach to designing generative audio models with customized controls capable of traversing out-of-distribution regions for novel sound synthesis. |
| title | Towards Controllable Audio Texture Morphing |
| topic | Audio and Speech Processing Artificial Intelligence Sound |
| url | https://arxiv.org/abs/2304.11648 |