In popular music, bass line tends to include relevant information about the chord sequence and thus segmenting musical audio data by bass notes can be used as a mid-level step to improve posterior higher level analysis, as chord detection and music structure analysis. In this paper, we present a comparison between four methods for detecting bass line onsets. The first method uses a multipitch detection algorithm to find the lowest note boundaries. The second method searches spectral differences in a low frequency range. The third uses Convolutional Neural Networks (CNN) and the fourth Recurrent Neural Networks (RNN). These methods are trained and tested on a MIDI rendered audio database, and standard evaluation metrics for detection problems are used, as well as a temporal accuracy for each method. The results are compared to other onset detection systems showing that the deep learning based methods have better performance and time accuracy. We believe that our work comparing standard approaches provides a useful insight on how onset detection methods can be adapted to specific kind of onsets.