This article presents a learning-based binaural source localization technique in the median plane and its application to robot audition. Binaural recordings capture the audio signal and acoustic transfer function from the source to the ears, known as the head-related transfer function (HRTF), which parameterizes spatial cues such as interaural time difference (ITD) and interaural level difference (ILD). ITD and ILD cues are prominent for source localization in the horizontal plane. Since ITD and ILD are nearly equal to zero in the median plane (the ear canal of both the ears is colocated), the localization is complex. Therefore, monaural spectral cues such as spectral notches are investigated for median plane source localization. The spectral notch represents the delay between the direct and the reflected wave. As it varies with the elevation angle, a learning-based model is developed to map the spectral notch with the elevation angle. The spectral notch features are extracted from the binaural recording using linear prediction cepstral coefficients (LPCC) and linear prediction residual coefficients (LPRC). Simulations and experiments are carried out using high-spatial-resolution HRTF measurements from CIPIC dataset to evaluate the performance. The results show a significant improvement in localization accuracy compared with existing methods.