Understanding Audio Analysis Differences
Different audio analysis systems may produce varying results for the same track due to their unique algorithms and methodologies. Here's a detailed explanation of these differences and how to convert between different scales.
Key Differences in Audio Features
Acousticness
Measures the likelihood of a track being performed with acoustic instruments versus electronic instruments.
- Different algorithms for detecting acoustic components
- Varying sensitivity to acoustic elements in complex mixes
- Impact of signal processing on acoustic detection
Energy
Represents the intensity and activity level of a track.
- Different approaches to measuring dynamic range
- Varying weights for frequency bands
- Different normalization methods
Instrumentalness
Predicts whether a track contains vocals or is purely instrumental.
- Different vocal detection algorithms
- Varying treatment of background vocals
- Different thresholds for instrumental classification
Loudness
Measures the overall volume level of a track.
- Different measurement scales (LUFS vs normalized)
- Varying approaches to peak normalization
- Different reference levels
Converting Between Scales
Conversion Formulas
acousticness = sound_value * 0.005 energy = sound_value * 2.25 instrumentalness = sound_value * 0.03 loudness = -(1 - sound_value) * 14
Example: "Blondie - Call me"
| Feature | SoundStat Value | Converted Value | Spotify Value |
|---|---|---|---|
| acousticness | 0.92 | 0.0046 | 0.000785 |
| energy | 0.36 | 0.81 | 0.824 |
| instrumentalness | 0.75 | 0.0225 | 0.00246 |
| loudness | 0.21 | -11.06 | -6.711 |
Note: Other parameters like tempo, key, and valence typically show good correspondence and don't require conversion. The conversion formulas provided are approximations and may need adjustment for specific use cases.