Objectively analyzing realistic music experiences remains a challenge in music cognition, with research focusing primarily on electroencephalographic (EEG) responses. One approach to determining whether the brain is processing and tracking a continuous stimulus like music is to reconstruct it, or decode it, from the neural data. Both speech and music have been successfully decoded from EEG responses using regularized linear modelling techniques where lagged response data is weighted and summed to estimate the stimulus (Zuk et al., 2021). Here, we investigate whether music can be decoded from cardiovascular signals, like inter-heart beat-intervals (RR intervals), which are mediated by the autonomic nervous system and have been shown to change in response to music (Koelsch & Jancke, 2015). To our knowledge, this decoding method has not yet been applied to electrocardiographic (ECG) responses to continuous music stimuli. With a continuous listening paradigm with acoustic music, we aim to determine if cardiovascular signals, alone or together with EEG, can be used to model and predict music amplitude envelopes over time, and how this compares to existing EEG-only approaches.