  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
