From beat tracking to beat expectation: Cognitive-based beat tracking for capturing pulse clarity through time.

Area of Science:

• Computational musicology
• Cognitive science
• Music information retrieval

Background:

• Musical pulse is fundamental to rhythm perception and organization.
• Pulse clarity, or the strength of the perceived beat, is crucial for analyzing musical affect.
• Existing computational models often lack temporal dynamics in analyzing rhythmic expectation.

Purpose of the Study:

• To present the Tactus Hypothesis Tracker (THT), a novel computational model for assessing pulse clarity in symbolic music over time.
• To develop a model that captures the dynamic evolution of rhythmic expectations and beat interpretations.
• To evaluate THT's performance against human perception and existing state-of-the-art models.

Main Methods:

• Developed THT based on beat tracking principles for symbolic rhythmic stimuli.
• Model generates beat interpretations, assigns fitness scores, and tracks their temporal evolution.
• Evaluated pulse clarity against human tapping variability and analyzed clarity dynamics on synthetic data.

Main Results:

• THT achieved results comparable to state-of-the-art pulse clarity models.
• The model demonstrated adaptability to changes in musical beat, showing appropriate "doubt" during estimation.
• Beat tracking by THT showed accurate phase estimation with a bias towards musically correct subdivisions.

Conclusions:

• THT provides a dynamic computational approach to understanding musical pulse clarity.
• The model effectively captures temporal changes in rhythmic expectation and beat perception.
• THT offers a valuable tool for musicological analysis and computational music research.