  ElectrodeNet, a deep learning based sound coding strategy for the cochlear
implant (CI), is proposed to emulate the advanced combination encoder (ACE)
strategy by replacing the conventional envelope detection using various
artificial neural networks. The extended ElectrodeNet-CS strategy further
incorporates the channel selection (CS). Network models of deep neural network
(DNN), convolutional neural network (CNN), and long short-term memory (LSTM)
were trained using the Fast Fourier Transformed bins and channel envelopes
obtained from the processing of clean speech by the ACE strategy. Objective
speech understanding using short-time objective intelligibility (STOI) and
normalized covariance metric (NCM) was estimated for ElectrodeNet using CI
simulations. Sentence recognition tests for vocoded Mandarin speech were
conducted with normal-hearing listeners. DNN, CNN, and LSTM based ElectrodeNets
exhibited strong correlations to ACE in objective and subjective scores using
mean squared error (MSE), linear correlation coefficient (LCC) and Spearman's
rank correlation coefficient (SRCC). The ElectrodeNet-CS strategy was capable
of producing N-of-M compatible electrode patterns using a modified DNN network
to embed maxima selection, and to perform in similar or even slightly higher
average in STOI and sentence recognition compared to ACE. The methods and
findings demonstrated the feasibility and potential of using deep learning in
CI coding strategy.
