Latent FxLMS: Accelerating Active Noise Control with Neural Adaptive Filters
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866914345454141440 |
|---|---|
| author | Sarkar, Kanad Lu, Austin Mittal, Manan Zhuang, Yongjie Corey, Ryan Singer, Andrew |
| author_facet | Sarkar, Kanad Lu, Austin Mittal, Manan Zhuang, Yongjie Corey, Ryan Singer, Andrew |
| contents | Filtered-X LMS (FxLMS) is commonly used for active noise control (ANC), wherein the soundfield is minimized at a desired location. Given prior knowledge of the spatial region of the noise or control sources, we could improve FxLMS by adapting along the low-dimensional manifold of possible adaptive filter weights. We train an auto-encoder on the filter coefficients of the steady-state adaptive filter for each primary source location sampled from a given spatial region and constrain the weights of the adaptive filter to be the output of the decoder for a given state of latent variables. Then, we perform updates in the latent space and use the decoder to generate the cancellation filter. We evaluate how various neural network constraints and normalization techniques impact the convergence speed and steady-state mean squared error. Under certain conditions, our Latent FxLMS model converges in fewer steps with comparable steady-state error to the standard FxLMS. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_03854 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Latent FxLMS: Accelerating Active Noise Control with Neural Adaptive Filters Sarkar, Kanad Lu, Austin Mittal, Manan Zhuang, Yongjie Corey, Ryan Singer, Andrew Machine Learning Sound Systems and Control Audio and Speech Processing Adaptation and Self-Organizing Systems Filtered-X LMS (FxLMS) is commonly used for active noise control (ANC), wherein the soundfield is minimized at a desired location. Given prior knowledge of the spatial region of the noise or control sources, we could improve FxLMS by adapting along the low-dimensional manifold of possible adaptive filter weights. We train an auto-encoder on the filter coefficients of the steady-state adaptive filter for each primary source location sampled from a given spatial region and constrain the weights of the adaptive filter to be the output of the decoder for a given state of latent variables. Then, we perform updates in the latent space and use the decoder to generate the cancellation filter. We evaluate how various neural network constraints and normalization techniques impact the convergence speed and steady-state mean squared error. Under certain conditions, our Latent FxLMS model converges in fewer steps with comparable steady-state error to the standard FxLMS. |
| title | Latent FxLMS: Accelerating Active Noise Control with Neural Adaptive Filters |
| topic | Machine Learning Sound Systems and Control Audio and Speech Processing Adaptation and Self-Organizing Systems |
| url | https://arxiv.org/abs/2507.03854 |