An important research field in neuroscience is understanding how we process individual sounds in natural environments that contain several sounds, often occurring simultaneously. The brain does this by identifying specific neural signals that correspond to each sound, allowing them to be analyzed individually. This study by Manting et al. uses frequency tagging and machine learning to separate neural responses to musical melodies and classify them according to specific melodies. Using two magnetoencephalography datasets, researchers found that the results were influenced by individual musical experience and task performance. Results indicated that increased selective attention for specific musical cues demonstrated a participant’s enhanced musicality. Additionally, there was a positive correlation with top-down attention in the left parietal cortex but negative correlation with bottom-up attention in the right prefrontal cortex. Bottom-up processing is when sensory information is used to draw conclusions, while top-down processing is when our previous experiences influence interpretation of sensory information. This means that the process of musical training allows individuals to be more efficient at top-down attention since they are able to apply prior experience to understand the sensory information they are receiving. In conclusion, this study demonstrates that the ability to separate, identify, and analyze neural signals is an ability that can be improved with training that takes advantage of top-down processing.