Missing data imputation for spectral audio signals


With the recent attention to audio processing in the time -frequency domain we increasingly encounter the problem of missing data. In this paper we present an approach that allows for imputing missing values in the time-frequency domain of audio signals. The presented approach is able to deal with real-world polyphonic signals by performing imputation even in the presence of complex mixtures. We show that this approach outperforms generic imputation approaches, and we present a variety of situations that highlight its utility.

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@article{Smaragdis2009MissingDI, title={Missing data imputation for spectral audio signals}, author={Paris Smaragdis and Bhiksha Raj and Madhusudana V. S. Shashanka}, journal={2009 IEEE International Workshop on Machine Learning for Signal Processing}, year={2009}, pages={1-6} }