Frequency Component Restoration for Music Sounds using a Markov Random Field and Maximum Entropy Learning

We propose a method that estimates frequency component structures from music signals with noise and restores them. Restoring frequency components hidden by other interfering sounds is a difficult problem but has become important in various music information processing systems for melody extraction and audio retrieval. The proposed method is based on a probabilistic model of a frequency component structure represented as a Markov random field. To design an appropriate model, we introduce a supervised learning technique based on the maximum entropy model. We tested the method using musical audio signals generated from notes played by real instruments and noises. For four of six instruments, the proposed method achieves F-measures greater than 0.44 even in periods where signals are replaced by noises. We also evaluated the method in terms of feature distortion recovery in audio fingerprint matching tasks. The results show that the proposed method clearly reduces the effect of noise on the similarity values