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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.16331 |
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| _version_ | 1866917621573615616 |
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| author | Yin, Hanzhi Cheng, Gang Steinmetz, Christian J. Yuan, Ruibin Stern, Richard M. Dannenberg, Roger B. |
| author_facet | Yin, Hanzhi Cheng, Gang Steinmetz, Christian J. Yuan, Ruibin Stern, Richard M. Dannenberg, Roger B. |
| contents | We describe a novel approach for developing realistic digital models of dynamic range compressors for digital audio production by analyzing their analog prototypes. While realistic digital dynamic compressors are potentially useful for many applications, the design process is challenging because the compressors operate nonlinearly over long time scales. Our approach is based on the structured state space sequence model (S4), as implementing the state-space model (SSM) has proven to be efficient at learning long-range dependencies and is promising for modeling dynamic range compressors. We present in this paper a deep learning model with S4 layers to model the Teletronix LA-2A analog dynamic range compressor. The model is causal, executes efficiently in real time, and achieves roughly the same quality as previous deep-learning models but with fewer parameters. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_16331 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Modeling Analog Dynamic Range Compressors using Deep Learning and State-space Models Yin, Hanzhi Cheng, Gang Steinmetz, Christian J. Yuan, Ruibin Stern, Richard M. Dannenberg, Roger B. Sound Machine Learning Audio and Speech Processing We describe a novel approach for developing realistic digital models of dynamic range compressors for digital audio production by analyzing their analog prototypes. While realistic digital dynamic compressors are potentially useful for many applications, the design process is challenging because the compressors operate nonlinearly over long time scales. Our approach is based on the structured state space sequence model (S4), as implementing the state-space model (SSM) has proven to be efficient at learning long-range dependencies and is promising for modeling dynamic range compressors. We present in this paper a deep learning model with S4 layers to model the Teletronix LA-2A analog dynamic range compressor. The model is causal, executes efficiently in real time, and achieves roughly the same quality as previous deep-learning models but with fewer parameters. |
| title | Modeling Analog Dynamic Range Compressors using Deep Learning and State-space Models |
| topic | Sound Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2403.16331 |