Benchmarking functional link expansions for audio classification tasks

Functional Link Artificial Neural Networks (FLANNs) have been extensively used for tasks of audio and speech classification, due to their combination of universal approximation capabilities and fast training. The performance of a FLANN, however, is known to be dependent on the specific functional link (FL) expansion that is used. In this paper, we provide an extensive benchmark of multiple FL expansions on several audio classification problems, including speech discrimination, genre classification, and artist recognition. Our experimental results show that a random-vector expansion is well suited for classification tasks, achieving the best accuracy in two out of three tasks.