In this work, a temporal convolutional network (TCN) based binaural reproduction of higher-order ambisonics (HOA) signals in the spherical harmonics (SH) domain is proposed. The binaural rendering is characterized by the head-related transfer function (HRTF). Since the HRTFs cannot be measured for all the directions, it limits error-free binaural reproduction. The proposed work presents a data-driven approach to learning binaural cues from the anthropometric parameter and source directions. The task is to estimate masking functions that transform the higher-order ambisonics (HOA) signals into binaural signals. The learning framework takes the HOA signals as the input along with the anthropometric parameters to generate the binaural signals. In the proposed method, the TCN implicitly learns the HRTFs parameter and produces the binaural signal. The performance of the method is evaluated based on the reproduction accuracy and mean square error (MSE). Further real-time experiments are carried out using the CIPIC HRTF dataset and the binaural recording using the autogenously developed bionic ears to validate the performance of the proposed method.