This paper presents a data-driven approach to automatic blind equalization of audio by predicting log-mel spectral features and deriving an inverse filter. The method uses a deep neural network, where a pre-trained model provides semantic embeddings as a backbone, and only a lightweight head is trained. This design improves training-time efficiency and generalization. Trained on both music and speech, the model is robust to noise and reverberation. An objective evaluation confirms its effectiveness, whereas a subjective test shows a performance comparable to an oracle that uses true log-mel spectral features, demonstrating its potential for real-world applications.
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https://aes2.org/publications/elibrary-page/?id=22996
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