Navigating the Intricacies of Markov Models in Sound

In our latest exploration within the “Mycelium and Sound Collectives” project, we dive deeper into the realm of machine learning, focusing on the pivotal step of feature engineering – transforming raw musical data into a machine-readable format. This process is crucial for our goals, ensuring the data highlights the musical characteristics essential for algorithmic learning and interpretation.

Today, we spotlight our third vlog, which delves into the intricacies of the Markov model through MAX/MSP, showcasing how the ml.markov object’s ‘order’ command significantly expands the model’s memory. This allows it to recognize and generate more complex musical patterns, revealing the potential of machine learning in music composition and improvisation.

This exploration not only enhances our understanding of Markov models but also highlights the importance of precise data preparation in machine learning. By improving how data is fed into the model, we can greatly enhance its predictive capabilities, offering new possibilities for musical creativity at the intersection of technology and art.

Stay tuned as we continue to push the boundaries of music, mycology, and machine learning, uncovering new insights and possibilities in this innovative project.