Blind Acoustic Parameter Estimation Through Task-Agnostic Embeddings Using Latent Approximations
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914891511627776 |
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| author | Götz, Philipp Tuna, Cagdas Brendel, Andreas Walther, Andreas Habets, Emanuël A. P. |
| author_facet | Götz, Philipp Tuna, Cagdas Brendel, Andreas Walther, Andreas Habets, Emanuël A. P. |
| contents | We present a method for blind acoustic parameter estimation from single-channel reverberant speech. The method is structured into three stages. In the first stage, a variational auto-encoder is trained to extract latent representations of acoustic impulse responses represented as mel-spectrograms. In the second stage, a separate speech encoder is trained to estimate low-dimensional representations from short segments of reverberant speech. Finally, the pre-trained speech encoder is combined with a small regression model and evaluated on two parameter regression tasks. Experimentally, the proposed method is shown to outperform a fully end-to-end trained baseline model. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_19989 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Blind Acoustic Parameter Estimation Through Task-Agnostic Embeddings Using Latent Approximations Götz, Philipp Tuna, Cagdas Brendel, Andreas Walther, Andreas Habets, Emanuël A. P. Audio and Speech Processing We present a method for blind acoustic parameter estimation from single-channel reverberant speech. The method is structured into three stages. In the first stage, a variational auto-encoder is trained to extract latent representations of acoustic impulse responses represented as mel-spectrograms. In the second stage, a separate speech encoder is trained to estimate low-dimensional representations from short segments of reverberant speech. Finally, the pre-trained speech encoder is combined with a small regression model and evaluated on two parameter regression tasks. Experimentally, the proposed method is shown to outperform a fully end-to-end trained baseline model. |
| title | Blind Acoustic Parameter Estimation Through Task-Agnostic Embeddings Using Latent Approximations |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2407.19989 |