Reducing the decay time of low-frequency resonances in small rooms remains a critical challenge in audio engineering, as these resonances can degrade sound quality, leading to a "boomy" or "muddy" audio experience. Traditional equalization methods, while effective in certain contexts, rely heavily on static equalization filters, making them suboptimal for complex scenarios. This paper explores a novel machine learning-based approach to suppress low-frequency resonances by leveraging a modified Wave-U-Net architecture. The model incorporates psychoacoustic principles, specifically perceptual modal thresholds, to ensure that resonance suppression is guided by human auditory perception while preserving the natural characteristics of the audio. A large dataset of synthetic Room Impulse Responses was generated using a physically informed parametric model for cuboid rooms with randomized dimensions and absorption profiles. The model was trained on amplitude-modulated white noise bursts to simulate broadband excitation. Evaluation on unseen synthetic data demonstrates effective suppression of low-frequency resonances below perceptual thresholds. Limitations in generalization are discussed, along with future directions that include retraining on musical stimuli and incorporating perceptual loss metrics.
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https://aes2.org/publications/elibrary-page/?id=23015
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