  The existing source cell-phone recognition method lacks the long-term feature
characterization of the source device, resulting in inaccurate representation
of the source cell-phone related features which leads to insufficient
recognition accuracy. In this paper, we propose a source cell-phone recognition
method based on spatio-temporal representation learning, which includes two
main parts: extraction of sequential Gaussian mean matrix features and
construction of a recognition model based on spatio-temporal representation
learning. In the feature extraction part, based on the analysis of time-series
representation of recording source signals, we extract sequential Gaussian mean
matrix with long-term and short-term representation ability by using the
sensitivity of Gaussian mixture model to data distribution. In the model
construction part, we design a structured spatio-temporal representation
learning network C3D-BiLSTM to fully characterize the spatio-temporal
information, combine 3D convolutional network and bidirectional long short-term
memory network for short-term spectral information and long-time fluctuation
information representation learning, and achieve accurate recognition of
cell-phones by fusing spatio-temporal feature information of recording source
signals. The method achieves an average accuracy of 99.03% for the closed-set
recognition of 45 cell-phones under the CCNU\_Mobile dataset, and 98.18% in
small sample size experiments, with recognition performance better than the
existing state-of-the-art methods. The experimental results show that the
method exhibits excellent recognition performance in multi-class cell-phones
recognition.
