This paper describes a new method for music onset detection. The novelty of the approach consists mainly of two elements: the time–frequency processing and the detection stages. The resonator time frequency image (RTFI) is the basic time–frequency analysis tool. The time–frequency processing part is in charge of transforming the RTFI energy spectrum into more natural energy change and pitch-change cues that are then used as input elements for the detection of music onsets by detection tools. Two detection algorithms have been developed: an energy-based algorithm and a pitch-based one. The energy-based detection algorithm exploits energy-change cues and performs particularly well for the detection of hard onsets. The pitch-based algorithm successfully exploits stable pitch cues for the onset detection in polyphonic music, and achieves much better performances than the energy-based algorithm when applied to the detection of soft onsets. Results for both the energy-based and pitch-based detection algorithms have been obtained on a large music dataset.