It is incredibly challenging to simultaneously locate an acoustic source in a noisy, reverberant environment and mitigates directional interference. The proposed study uses a spherical harmonic decomposition method to determine the spherical harmonics phase magnitude (SH-PM) components corresponding to the received spherical microphone array (SMA) signals. Before SH-PM components are used as input features to the CNN model, binary masking removes directional interference and emphasizes the desired audio source. In this work, the binary mask is estimated using the learning technique such that it is possible to reliably discriminate between acceptable and undesired sources using real-time mask estimation. The proposed strategy creates a learning-based mask to enable real-time and reliable filtering of the undesirable source. Because of this, the entire strategy is extremely flexible and adaptable. By creating datasets, extensive simulations evaluate the effectiveness of the offered strategy. Additionally, the approach is experimentally validated by conducting tests in a live lab setting. The significance of the suggested strategy promotes the use of the technique in real-world situations.