Locating the source of low-frequency forced oscillation is an important aspect of bulk power system operation, and it facilitates operator’s intervention to the faulty component and timely implementation of mitigation measures. In this paper, a novel deep learning-based forced oscillation source locator is proposed to infer the oscillation-source using data from phasor measurement units (PMU). The locator is trained offline using the spectral information extracted from the sliding-window time-series data from simulated PMU measurements over a range of randomly chosen oscillatory events. The effectiveness of the proposed method has been validated on the Western Electricity Coordinating Council (WECC) 179-bus test system, comparing with an existing energy-based method. Noise-robustness of the method has also been evaluated.