Harmonic complex tones (HCTs) found in speech, music, and animal vocalizations evoke strong pitch percepts at their fundamental frequencies. The strongest pitches are produced by HCTs that contain harmonics resolved by cochlear frequency analysis, but HCTs containing solely unresolved harmonics also evoke a weaker pitch at their envelope repetition rate (ERR). In the auditory periphery, neurons phase lock to the stimulus envelope, but this temporal representation of ERR degrades and gives way to rate codes along the ascending auditory pathway. To assess the role of the inferior colliculus (IC) in such transformations, we recorded IC neuron responses to HCT and sinusoidally modulated broadband noise (SAMN) with varying ERR from unanesthetized rabbits. Different interharmonic phase relationships of HCT were used to manipulate the temporal envelope without changing the power spectrum. Many IC neurons demonstrated band-pass rate tuning to ERR between 60 and 1,600 Hz for HCT and between 40 and 500 Hz for SAMN. The tuning was not related to the pure-tone best frequency of neurons but was dependent on the shape of the stimulus envelope, indicating a temporal rather than spectral origin. A phenomenological model suggests that the tuning may arise from peripheral temporal response patterns via synaptic inhibition. We also characterized temporal coding to ERR. Some IC neurons could phase lock to the stimulus envelope up to 900 Hz for either HCT or SAMN, but phase locking was weaker with SAMN. Together, the rate code and the temporal code represent a wide range of ERR, providing strong cues for the pitch of unresolved harmonics.NEW & NOTEWORTHY Envelope repetition rate (ERR) provides crucial cues for pitch perception of frequency components that are not individually resolved by the cochlea, but the neural representation of ERR for stimuli containing many harmonics is poorly characterized. Here we show that the pitch of stimuli with unresolved harmonics is represented by both a rate code and a temporal code for ERR in auditory midbrain neurons and propose possible underlying neural mechanisms with a computational model.