A sensor transformation attention network (STAN) model-based system for classifying input signals including any one or any combination of a raw signal corresponding to any one or any combination of an audio feature and a visual feature and a preprocessed signal of the audio feature and the visual feature, said STAN model-based system trained for directing attention to a sensor with a lower noise level, said STAN model-based system comprising: a plurality of sensors configured to collect the input signals, said plurality of sensors with dynamically changing noise levels; a plurality of attention modules configured to calculate attention scores respectively corresponding to feature vectors respectively corresponding to the input signals, wherein the feature vectors are obtained by mapping the input signals to a feature space, said input signals being partitioned on a per-frame basis, and wherein the attention scores are calculated by converting the feature vectors to one scalar attention score per frame of the feature vectors by using a non-flattening dense layer; a merge module configured to calculate attention values of the attention scores by performing a softmax operation operating on the attention scores of the plurality of sensors, and generate a merged transformation vector by scaling the feature vectors based on the corresponding attention values and merging the scaled feature vectors using an adding operation; a task-specific module configured to classify the merged transformation vector; and wherein each of the attention modules corresponds to one of the plurality of the sensors, respectively, and the STAN model is trained based on a training set in which noise sampled from normally distributed random noise using a random walk noise model is mixed with the input signals, resulting in the scalar attention scores of the attention modules having a negative correlation with noise levels of the plurality of sensors such that, when a noise level for a sensor of the plurality of sensor increases, the scalar attention scores corresponding to said sensor decrease, and vice versa. a plurality of sensors configured to collect the input signals, said plurality of sensors with dynamically changing noise levels; a plurality of attention modules configured to calculate attention scores respectively corresponding to feature vectors respectively corresponding to the input signals, wherein the feature vectors are obtained by mapping the input signals to a feature space, said input signals being partitioned on a per-frame basis, and wherein the attention scores are calculated by converting the feature vectors to one scalar attention score per frame of the feature vectors by using a non-flattening dense layer; a merge module configured to calculate attention values of the attention scores by performing a softmax operation operating on the attention scores of the plurality of sensors, and generate a merged transformation vector by scaling the feature vectors based on the corresponding attention values and merging the scaled feature vectors using an adding operation; a task-specific module configured to classify the merged transformation vector; and wherein each of the attention modules corresponds to one of the plurality of the sensors, respectively, and the STAN model is trained based on a training set in which noise sampled from normally distributed random noise using a random walk noise model is mixed with the input signals, resulting in the scalar attention scores of the attention modules having a negative correlation with noise levels of the plurality of sensors such that, when a noise level for a sensor of the plurality of sensor increases, the scalar attention scores corresponding to said sensor decrease, and vice versa. <EOS>