Evaluating music sequence models through missing data
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.