We introduce a new and intuitive algorithm to characterize and localize multiple harmonic sources intersecting in the spatial and frequency domains. It jointly estimates their fundamental frequencies, their respective amplitudes, and their directions of arrival based on an intelligent non-parametric signal representation. To obtain these parameters, we first apply variable-scale sampling on unbiased cross-correlation functions between pairs of microphone signals to generate a joint parameter space. Then, we employ a multidimensional maxima detector to represent the parameters in a sparse joint parameter space. In comparison to others, our algorithm solves the issue of pitch-period doubling when using cross-correlation functions, it estimates multiple harmonic sources with a signal power smaller than the signal power of the dominant harmonic source, and it associates the estimated parameters to their corresponding sources in a multidimensional sparse joint parameter space, which can be directly fed into a tracker. We tested our algorithm and three others on synthetic data and speech data recorded in a real reverberant environment and evaluated their performance by employing the joint recall measure, the root-mean-square error, and the cumulative distribution function of fundamental frequencies and directions of arrival. The evaluations show promising results: Our algorithm outperforms the others in terms of the joint recall measure, and it can achieve root-mean-square errors of one Hertz or one degree and smaller, which facilitates, e.g., distant-speech enhancement or source separation.