The decision result of improved Multi-SVDD.

During the real-time recognition of porcine abnormal sounds, the accuracy and stability of the recognition method are crucial to guarantee a good performance. For this purpose, an improved Multiple-Support Vector Data Description (Multi-SVDD) is proposed in this paper. Firstly, the improved spectral subtraction using improved Minima Controlled Recursive Averaging (IMCRA) and Spectral Subtraction (SS) is applied to remove the noise of collected sounds. Then, the Mel-Frequency Cepstral Coefficients (MFCC) and first-order differential MFCC (ΔMFCC) are extracted as feature parameters. Finally, the Multi-SVDD is used to detect and recognize the porcine abnormal sounds. In order to improve the accuracy and error-tolerance of Multi-SVDD for human errors on tagging data, the space density information of training data is calculated as the confidences to reduce the interference of outliers in the process of Multi-SVDD training. The experimental results show that the accuracy, precision and recall of the proposed method are as high as 95.0%, 95.4% and 95.0% respectively, which indicates higher error-tolerance capability than classical SVDD.