Feature Extraction for the Prediction of Multichannel

This paper seeks to present an algorithm for the pre- diction of frontal spatial fidelity and surround spatial fidelity of multichannel audio, which are two attributes of the subjective pa- rameter called basic audio quality. A number of features chosen to represent spectral and spatial changes were extracted from a set of recordings and used in a regression model as independent variables for the prediction of spatial fidelities. The calibration of the model was done by ridge regression using a database of scores obtained from a series of formal listening tests. The statistically significant features based on interaural cross correlation and spec- tral features found from an initial model were employed to build a simplified model and these selected features were validated. The results obtained from the validation experiment were highly cor- related with the listening test scores and had a low standard error comparable to that encountered in typical listening tests. The ap- plicability of the developed algorithm is limited to predicting the basic audio quality of low-pass filtered and down-mixed recordings (as obtained in listening tests based on a multistimulus test para- digm with reference and two anchors: a 3.5-kHz low-pass filtered signal and a mono signal).

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