Abstract: To solve the problem of source cell-phone identification under practical environmental noises, a source cell-phone identification method based on linear discriminant analysis and temporal convolutional network is proposed. Firstly, the classification performance of different speech features under practical noises is analyzed in detail, based on which a new mixed speech feature is proposed according to band energy descriptor, constant Q transform, and linear discriminant analysis. Additionally, the mixed speech feature is used as the input to the temporal convolutional network for training and classification. Finally, the test results on the practical noise speech database of 10 brands, 47 mobile phone models, and 32,900 speech samples show that the average recognition accuracy of the proposed method reaches 99.82%. Moreover, compared with the two existing classical methods based on the band energy descriptor and support vector machine, and the constant Q transform domain and convolutional neural network, the proposed method increases the average recognition accuracy by about 0.44 and 0.54 percentages respectively, the average recall by about 0.45 and 0.55 percentages respectively, the average precision by about 0.41 and 0.57 percentages respectively, and the average F1-score by about 0.49 and 0.55 percentages respectively. The experimental results show that the proposed method has better comprehensive identification performance.

Key words: source cell-phone identification, practical environmental noise, mixed feature, linear discriminant analysis, temporal convolutional network