Depression has become one of the main afflictions that threaten people's mental health. However, the current traditional diagnosis methods have certain limitations, so it is necessary to find a method of objective evaluation of depression based on intelligent technology to assist in the early diagnosis and treatment of patients. Because the abnormal speech features of patients with depression are related to their mental state to some extent, it is valuable to use speech acoustic features as objective indicators for the diagnosis of depression. In order to solve the problem of the complexity of speech in depression and the limited performance of traditional feature extraction methods for speech signals, this article suggests a Three-Dimensional Convolutional filter bank with Highway Networks and Bidirectional GRU (Gated Recurrent Unit) with an Attention mechanism (in short 3D-CBHGA), which includes two key strategies. (1) The three-dimensional feature extraction of the speech signal can timely realize the expression ability of those depression signals. (2) Based on the attention mechanism in the GRU network, the frame-level vector is weighted to get the hidden emotion vector by self-learning. Experiments show that the proposed 3D-CBHGA can well establish mapping from speech signals to depression-related features and improve the accuracy of depression detection in speech signals.

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Differential Evolution (abbreviation for DE) is showing many advantages in solving optimization problems, such as fast convergence, strong robustness, and so on. However, when DE faces a complex target space, the diversity of its population will degenerate in a small scope; even sometimes it is premature to fall into the local minimum. All things contend in beauty in the world; a Shuffled Frog Leaping Algorithm (abbreviation for SFLA) has a strong global ability; unfortunately, its convergence speed is also slow. In order to overcome the shortcoming, this article suggests a Shuffled Frog-leaping Differential Evolution (abbreviation for SFDE) algorithm in a cognitive radio network, which combines Differential Evolution with Shuffled Frog Leaping Algorithm. This proposed method hikes its local searching for a certain number of subgroups, and their individuals join together and share their mutual information among different subgroups, which improves the population diversity and achieves the purpose of fast global search during the whole Differential Evolution. The SFDE is examined by 20 well-known numerical benchmark functions, and those obtained results are compared with four other related algorithms. The experimental simulation in solving the problem of effective throughput optimization for cognitive users shows that the proposed SFDE is effective.

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