Note that using complex numbers the number of network parameters duplicates. In general, we can say that a network with n complex nodes will have a training cost equivalent to a network with 2*n real nodes.

About audio, it is true that a lot of analytics is done in Fourier/Laplace spaces, where complex numbers are mandatory. However, the incoming signal is real, also filters can be keep on this field. It is not usual, by example, the need of a DFT followed by a complex number NN.

Finally, note one of the main advantages of a NN is their non-linear structure and capabilities. Usual transforms and related tools are restricted to linear. In this point, the NNs are a step after.

Note that using complex numbers doubles the number of network parameters. In general, we can say that a network with $n$ complex nodes will have a training cost equivalent to a network with $2n$ real nodes.

About audio, it is true that a lot of analytics is done in Fourier/Laplace spaces, where complex numbers are mandatory. However, the incoming signal is real, also filters can be keep on this field. It is not usual, by example, the need of a DFT followed by a complex number NN.

Finally, note one of the main advantages of a NN is their non-linear structure and capabilities. Usual transforms and related tools are restricted to linear. In this point, the NNs are a step after.