Onsets are criterion points to separate an audio signal into several notes. In this paper, we combine the advantages of conventional rule-based onset detection methods and convolutional neural network (CNN) based methods and propose an advanced onset detection algorithm. Different from rule-based methods, we apply the CNN to avoid tuning thresholds empirically. Different from existing CNN-based methods, which apply the original signal as the input directly, we construct a data with 204 feature layers and use it as the CNN input. Simulations show that the proposed algorithm has much better performance than both rule-based and existing CNN-based onset detection methods.