A novel approach based on a modified mask R-CNN for the weight prediction of live pigs

Since determining the weight of pigs during large-scale breeding and production is challenging,using non-contact estimation methods is vital.This study proposed a novel pig weight prediction method based on a mod-ified mask region-convolutional neural network(mask R-CNN).The modified approach used ResNeSt as the backbone feature extraction network to enhance the image feature extraction ability.The feature pyramid net-work(FPN)was added to the backbone feature extraction network for multi-scale feature fusion.The channel at-tention mechanism(CAM)and spatial attention mechanism(SAM)were introduced in the region proposal network(RPN)for the adaptive integration of local features and their global dependencies to capture global in-formation,ultimately improving image segmentation accuracy.The modified network obtained a precision rate(P),recall rate(R),and mean average precision(MAP)of 90.33%,89.85%,and 95.21%,respectively,effectively segmenting the pig regions in the images.Five image features,namely the back area,body length,body width,average depth,and eccentricity,were investigated.The pig depth images were used to build five regression algo-rithms(ordinary least squares(OLS),AdaBoost,CatBoost,XGBoost,and random forest(RF))for weight value pre-diction.AdaBoost achieved the best prediction result with a coefficient of determination(R^(2))of 0.987,a mean absolute error(MAE)of 2.96 kg,a mean square error(MSE)of 12.87 kg^(2),and a mean absolute percentage error(MAPE)of 8.45%.The results demonstrated that the machine learning models effectively predicted the weight values of the pigs,providing technical support for intelligent pig farm management.

A method of whispered speech enhancement based on speech absence probability and modified mel-domain masking model

Whispered speech enhancement using auditory masking model in modified Mel- domain and Speech Absence Probability （SAP） was proposed. In light of the phonation char- acteristic of whisper, we modify the Mel-frequency Scaling model. Whispered speech is filtered by the proposed model. Meanwhile, the value of masking threshold for each frequency band is dynamically determined by speech absence probability. Then whispered speech enhancement is conducted by adaptively rectifying the spectrum subtraction coefficients using different masking threshold values. Results of objective and subjective tests on the enhanced whispered signal show that compared with other methods; the proposed method can enhance whispered signal with better subjective auditory quality and less distortion by reducing the music noise and background noise under the masking threshold value.