## Radar Chart: Evaluation on MARBLE Benchmark ### Overview The radar chart compares the performance of three models (MERT, MusicFM [MSD], and MuQ [Ours]) across seven evaluation dimensions on the MARBLE benchmark. The chart uses a circular layout with axes representing evaluation categories and radial distance indicating performance scores (0–100). Three colored lines represent the models, with MuQ (green) consistently outperforming others in specific categories. ### Components/Axes - **Axes (Categories)**: 1. Emotion/Arousal 2. Emotion/Valence 3. Key 4. Genre 5. Pitch 6. Instrument 7. Tagging/ROC - **Legend**: - MERT (orange) - MusicFM (MSD) (blue) - MuQ (Ours) (green) - **Axis Markers**: - Radial scale from 0 to 100 (increments of 10). - Axial labels positioned clockwise around the perimeter. ### Detailed Analysis - **MERT (orange line)**: - Peaks at **Tagging/ROC (90)** and **Pitch (85)**. - Lowest in **Emotion/Valence (60)**. - Moderate performance in **Genre (70)** and **Instrument (75)**. - **MusicFM (MSD) (blue line)**: - Highest in **Singer (85)**. - Lowest in **Emotion/Valence (65)**. - Strong in **Genre (80)** and **Pitch (85)**. - **MuQ (Ours) (green line)**: - Dominates **Tagging/ROC (95)** and **Singer (90)**. - Slight dip in **Emotion/Valence (70)**. - Strong in **Genre (85)** and **Instrument (80)**. ### Key Observations 1. **MuQ (Ours)** achieves the highest scores in **Tagging/ROC (95)** and **Singer (90)**, suggesting superior performance in these categories. 2. **MERT** excels in **Tagging/ROC (90)** but struggles with **Emotion/Valence (60)**. 3. **MusicFM (MSD)** performs best in **Singer (85)** but has notable weaknesses in **Emotion/Valence (65)** and **Instrument (70)**. 4. All models show variability in **Emotion/Arousal** (MERT: 75, MusicFM: 70, MuQ: 80), indicating inconsistent emotional analysis. ### Interpretation The chart highlights **MuQ (Ours)** as the most robust model overall, particularly in **Tagging/ROC** and **Singer**, where it outperforms others by margins of 5–15 points. **MERT** demonstrates balanced but suboptimal performance, while **MusicFM (MSD)** excels in singer-related tasks but falters in emotional valence and instrument recognition. The disparity in **Emotion/Valence** scores across models suggests a critical area for improvement in affective computing applications. The data implies that model selection should prioritize **MuQ** for tasks requiring tagging or singer identification, while **MusicFM** may be preferable for genre/pitch analysis despite its emotional valence limitations.