Audio imputation using the non-negative hidden markov model

Authors:
Jinyu Han;Gautham J. Mysore;Bryan Pardo
Affiliations:
EECS Department, Northwestern University;Advanced Technology Labs, Adobe Systems Inc.;EECS Department, Northwestern University
Venue:
LVA/ICA'12 Proceedings of the 10th international conference on Latent Variable Analysis and Signal Separation
Year:
2012

Citing 3
Cited 0

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Missing Data Imputation for Time-Frequency Representations of Audio Signals

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Abstract

Missing data in corrupted audio recordings poses a challenging problem for audio signal processing. In this paper we present an approach that allows us to estimate missing values in the time-frequency domain of audio signals. The proposed approach, based on the Non-negative Hidden Markov Model, enables more temporally coherent estimation for the missing data by taking into account both the spectral and temporal information of the audio signal. This approach is able to reconstruct highly corrupted audio signals with large parts of the spectrogram missing. We demonstrate this approach on real-world polyphonic music signals. The initial experimental results show that our approach has advantages over a previous missing data imputation method.