An initial feasibility study is presented exploring the use of a pre-trained feature extractor designed for large-scale audio classification applied to the task of predicting colouration between binaural signals. A multilayer perceptron (MLP) is trained to predict binaural colouration using feature embeddings obtained from the VGGish network and data from five previously conducted listening tests. The evaluation compares seven versions of the network, each trained using different data augmentation methods, along with three existing signal processing methods for predicting binaural colouration: basic spectral difference (BSD), log. spectral distance (LSD) and an auditory model for predicting binaural colouration (PBC-2). Results show that while the MLP networks are comparable to BSD and LSD, specific features relevant for colouration may be needed to compete against the more complex PBC-2.
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https://aes2.org/publications/elibrary-page/?id=23007
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