Aiming at the problem that the current audio steganography detection methods have low accuracy in detecting audio steganography based on Syndrome-Trellis Codes (STC), and considering the advantages of Convolutional Neural Network (CNN) in extracting abstract features, a model for audio steganography detection combining Deep Residual Network (DRN) and eXtreme Gradient Boosting (XGBoost) was proposed. Firstly, a fixed-parameter High-Pass Filter (HPF) was used to preprocess the input audio, and features were extracted through three convolutional layers. Truncated Linear Unit (TLU) activation function was applied in the first convolutional layer to make the model adapt to the distribution of steganographic signals with low Signal-To-Noise Ratio (SNR). Then, the abstract features were further extracted by five-stage residual blocks and pooling operations. Finally, the extracted high-dimensional features were classified as inputs of the XGBoost model through fully connected layers and dropout layers. The STC steganography and the Least Significant Bit Matching (LSBM) steganography were detected respectively. Experimental results showed that when the embedding rates were 0.5 bps (bit per sample), 0.2 bps and 0.1 bps respectively, that is to say, the average number of bits modified for per audio sample equaled to 0.5, 0.2 and 0.1 respectively, the proposed model achieved average detection accuracies of 73.27%, 70.16% and 65.18% respectively for the STC steganography with a sub check matrix with height of 7, and the average detection accuracies of 86.58%, 76.08% and 72.82% respectively for the LSBM steganography. Compared with the traditional steganography detection methods based on extracting handcrafted features and deep learning steganography detection methods, the proposed model has the average detection accuracies for the two steganography algorithms both increased by more than 10 percent points.