Rethinking Non-Negative Matrix Factorization with Implicit Neural Representations
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866916833273053184 |
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| author | Subramani, Krishna Smaragdis, Paris Higuchi, Takuya Souden, Mehrez |
| author_facet | Subramani, Krishna Smaragdis, Paris Higuchi, Takuya Souden, Mehrez |
| contents | Non-negative Matrix Factorization (NMF) is a powerful technique for analyzing regularly-sampled data, i.e., data that can be stored in a matrix. For audio, this has led to numerous applications using time-frequency (TF) representations like the Short-Time Fourier Transform. However extending these applications to irregularly-spaced TF representations, like the Constant-Q transform, wavelets, or sinusoidal analysis models, has not been possible since these representations cannot be directly stored in matrix form. In this paper, we formulate NMF in terms of learnable functions (instead of vectors) and show that NMF can be extended to a wider variety of signal classes that need not be regularly sampled. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_04439 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Rethinking Non-Negative Matrix Factorization with Implicit Neural Representations Subramani, Krishna Smaragdis, Paris Higuchi, Takuya Souden, Mehrez Audio and Speech Processing Machine Learning Sound Non-negative Matrix Factorization (NMF) is a powerful technique for analyzing regularly-sampled data, i.e., data that can be stored in a matrix. For audio, this has led to numerous applications using time-frequency (TF) representations like the Short-Time Fourier Transform. However extending these applications to irregularly-spaced TF representations, like the Constant-Q transform, wavelets, or sinusoidal analysis models, has not been possible since these representations cannot be directly stored in matrix form. In this paper, we formulate NMF in terms of learnable functions (instead of vectors) and show that NMF can be extended to a wider variety of signal classes that need not be regularly sampled. |
| title | Rethinking Non-Negative Matrix Factorization with Implicit Neural Representations |
| topic | Audio and Speech Processing Machine Learning Sound |
| url | https://arxiv.org/abs/2404.04439 |