Separating vocal elements from musical tracks is a longstanding challenge in audio signal processing. This study tackles the distinct separation of vocal components from musical spectrograms. We employ the Short Time Fourier Transform (STFT) to extract audio waves into detailed frequency-time spectrograms, utilizing the benchmark MUSDB18 dataset for music separation. Subsequently, we implement a UNet neural network to segment the spectrogram image, aiming to delineate and extract singing voice components accurately. We achieved noteworthy results in audio source separation using of our U-Net-based models. The combination of frequency-axis normalization with Min/Max scaling and the Mean Absolute Error (MAE) loss function achieved the highest Source-to-Distortion Ratio (SDR) of 7.1 dB, indicating a high level of accuracy in preserving the quality of the original signal during separation. This setup also recorded impressive Source-to-Interference Ratio (SIR) and Source-to-Artifact Ratio (SAR) scores of 25.2 dB and 7.2 dB, respectively. These values significantly outperformed other configurations, particularly those using Quantile-based normalization or a Mean Squared Error (MSE) loss function. Our source code, model weights, and demo material can be found at the project's GitHub repository: https://github.com/mbrotos/SoundSeg