Controlling the Parameterized Multi-channel Wiener Filter using a tiny neural network
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908455606943744 |
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| author | Grinstein, Eric Pandey, Ashutosh Li, Cole Srinivas, Shanmukha Azcarreta, Juan Donley, Jacob Lee, Sanha Aroudi, Ali Bilen, Cagdas |
| author_facet | Grinstein, Eric Pandey, Ashutosh Li, Cole Srinivas, Shanmukha Azcarreta, Juan Donley, Jacob Lee, Sanha Aroudi, Ali Bilen, Cagdas |
| contents | Noise suppression and speech distortion are two important aspects to be balanced when designing multi-channel Speech Enhancement (SE) algorithms. Although neural network models have achieved state-of-the-art noise suppression, their non-linear operations often introduce high speech distortion. Conversely, classical signal processing algorithms such as the Parameterized Multi-channel Wiener Filter ( PMWF) beamformer offer explicit mechanisms for controlling the suppression/distortion trade-off. In this work, we present NeuralPMWF, a system where the PMWF is entirely controlled using a low-latency, low-compute neural network, resulting in a low-complexity system offering high noise reduction and low speech distortion. Experimental results show that our proposed approach results in significantly better perceptual and objective speech enhancement in comparison to several competitive baselines using similar computational resources. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_13863 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Controlling the Parameterized Multi-channel Wiener Filter using a tiny neural network Grinstein, Eric Pandey, Ashutosh Li, Cole Srinivas, Shanmukha Azcarreta, Juan Donley, Jacob Lee, Sanha Aroudi, Ali Bilen, Cagdas Sound Audio and Speech Processing Noise suppression and speech distortion are two important aspects to be balanced when designing multi-channel Speech Enhancement (SE) algorithms. Although neural network models have achieved state-of-the-art noise suppression, their non-linear operations often introduce high speech distortion. Conversely, classical signal processing algorithms such as the Parameterized Multi-channel Wiener Filter ( PMWF) beamformer offer explicit mechanisms for controlling the suppression/distortion trade-off. In this work, we present NeuralPMWF, a system where the PMWF is entirely controlled using a low-latency, low-compute neural network, resulting in a low-complexity system offering high noise reduction and low speech distortion. Experimental results show that our proposed approach results in significantly better perceptual and objective speech enhancement in comparison to several competitive baselines using similar computational resources. |
| title | Controlling the Parameterized Multi-channel Wiener Filter using a tiny neural network |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2507.13863 |