Project 2 survey result 

Project 2 Proposal <Here>

In-class Activity <Here>

End Presentation for Project 2 <Here>

Github <Here>

Overview

This report summarizes the findings from a survey comparing traditional 2D sheet music with an augmented reality (AR) music visualization in terms of clarity, engagement, immersion, cognitive load, and pattern recognition. Participants were divided into Group 1 (Song A in 2D, Song B in AR) and Group 2 (Song A in AR, Song B in 2D). The goal was to assess how different visualization methods affect music comprehension and user experience.

Key Findings

1. Accuracy in Pattern Recognition & Time Spent 

• Group 1 (Song A in 2D): 3.9/10, 54.17 sec

• Group 2 (Song A in AR): 4.71/10, 47.0 sec

• Group 1 (Song B in AR): 3.89/10, 85.33 sec

• Group 2 (Song B in 2D): 6.79/10, 59.67 sec

Insight:

• AR was rated highest (8.0) when experienced first (Group 2).

• 2D was more effective when introduced first (6.3 vs. 5.6 for AR in Group 1).

• Suggests AR may require initial familiarization but can outperform 2D once learned.

• 2D was consistently clearer (5.5–6.5) due to familiarity.

• AR clarity improved when used first (7.2 vs. 4.3), indicating a learning curve.

2. Quick Overview

3. Reliance on Visual vs. Audio Cues

Insight:

• 2D notation encouraged audio reliance (users listened more).

• AR encouraged visual learning, especially when introduced first.

4. Engagement & Immersion

Insight:

• AR was significantly more engaging (8.3) and immersive (7.3) than 2D.

• Users found AR more interactive and memorable, especially when introduced first.

6. Reference Over Traditional Sheet Music

• Group 1 (2D vs. AR): 6.8 (2D) vs. 3.5 (AR)

• Group 2 (AR vs. 2D): 6.8 (AR) vs. 6.3 (2D)

Insight:

• When AR was experienced first, users rated it higher (6.8) than traditional notation.

• Users who saw 2D first preferred it (6.8 vs. 3.5 for AR), suggesting initial exposure influences preference.

Participant Feedback

Strengths of AR Visualization

• "The AR experience felt like looking at an art piece."

• "Helped me understand high vs. low notes better."

• "More immersive and interactive than static sheet music."

Areas for Improvement

• Depth interpretation: "Having depth for pitch was not intuitive."

• Note persistence: "Notes disappearing quickly made tracking difficult."

• Axis labeling: "It wasn’t clear what the bars represented in AR."

conclusion

1. AR Excels with Complex, Energetic Music (Song A)

Characteristics:

• Intense, energetic tone

• Broad pitch range (33/A1 to 80/G#5)

• Three distinct tracks creating rich visual features

Performance in AR:

• Higher structure comprehension (8.0 vs 6.3 in 2D)

• Better clarity (7.2 vs 5.5 in 2D)

• More engaging (8.3) and immersive (7.3)

Why it works:

• AR's spatial representation helps users:

• Distinguish multiple concurrent melodies

• Track wide pitch variations more intuitively

• Engage with the music's dramatic dynamics

2. 2D Suffices for Subtle, Repetitive Music (Song B)

Characteristics:

• Calm, subdued tone

• Narrower pitch range (55/G3 to 84/C6)

• Two tracks with less variation

Performance in AR:

• Lower clarity (4.3 vs 6.5 in 2D)

• No engagement advantage (6.5 vs 4.5 in 2D)

• Higher mental demand (6.5 vs 3.8)

Why AR underperforms:

• The music's simplicity made AR's dimensionality:

• Unnecessary for pattern recognition

• Potentially distracting for tracking limited variations

• More mentally taxing without proportional benefit

The effectiveness of music visualization depends fundamentally on the composition's characteristics. AR provides significant benefits for complex, energetic pieces like Beethoven's Moonlight Sonata, where its spatial representation helps users navigate intricate musical structures. However, for simpler, more repetitive music, traditional 2D notation remains more efficient and less cognitively demanding.