Abstract: Disclosed are a signal processing method and an abnormal sound detection system. First, a neural network model is trained, including: (a) randomly selecting a plurality of sample signals from a training database to obtain a combined signal, wherein the training database includes the first sample set belonging to the first classification label and the second sample set belonging to the second classification label, and the number of selected sample signals conforms to the preset number; repeating the said step to obtain a plurality of combined signals and using the combined signals to train the neural network model. Then, a sound signal received from a sound receiving apparatus is inputted to the trained neural network model to output a probability value, and a corresponding notification signal is outputted based on the probability value.

Abstract: A simulated-evolution-based macro refinement method includes evaluating a score of each placed macro cell to be refined; generating a random number; determining whether the score satisfies a predetermined condition; placing the macro cell into a queue if the score associated with the macro cell satisfies the predetermined condition; and sorting and placing macro cells of the queue according to scores of the macro cells in the queue.