Analysis
There are three principal methods for identifying a song’s emotional character, each offering a distinct pathway to classification: a qualitative, an analytical, and a technical approach.
The Somatic Approach: Feeling the Bhāva
This method is based on introspection and interoception—the sense of the internal state of your body:
Listen Mindfully: Play the track and close your eyes. Avoid analysing the music and instead focus on the feelings and physical sensations that arise within you.
Sense the Inner Experience: Does the music make you feel expansive and joyful, or contracted and sad? Does it energise you and make you want to move, or does it calm you into stillness? Allow your body to move to it.
Consult the Framework: With this felt sense in mind, review the “Inner Experience”, “Outer Expression”, and “Psychological Description” columns of the Sonic Bhāva framework. Find the bhāva that most closely matches your own experience of the song.
The Analytical Approach: Dimensional Placement
If you prefer a more analytical method, you can place the song within the three-dimensional VED space. For the song you are listening to, assign a value between 0.0 and 1.0 for each dimension:
Valence (V): How pleasant is it? (0.0 for unpleasant, 1.0 for pleasant).
Energy (E): How stimulating is it? (0.0 for calming, 1.0 for energising).
Dominance (D): How in control or powerful does it make you feel? (0.0 for submissive, 1.0 for dominant).
Once you have your estimated coordinates (e.g., V: 0.75, E: 0.70, D: 0.50), you can browse the framework data to find the bhāva with the most similar VED values (e.g., in this case, “Playfulness / Mischief”).
When the VED values for a track have been derived—whether intuitively, via an algorithm, or from a third-party analysis tool—the centroid model offers a mathematical way of classifying the song’s primary emotion.
The centroid model works by treating a song’s average VED coordinates as a point in 3D space. It then calculates the straight-line distance from this point to the predefined coordinates of every bhāva in our framework. The bhāva with the shortest distance is the song’s closest emotional match.
The recommended classification method uses the Euclidean distance in all three dimensions (Valence, Energy, and Dominance) to find the nearest bhāva.
The formula for the distance $d$ between the song’s VED coordinates (Vs, Es, Ds) and a bhāva’s coordinates (Vb, Eb, Db) is calculated as:
$$ \text{Distance} = \sqrt{(V_s - V_b)^2 + (E_s - E_b)^2 + (D_s - D_b)^2} $$
By calculating this distance for all 26 bhāvas, the one with the minimum value is identified as the song’s primary emotion.
The approximate classification method uses the Euclidean distance in two dimensions (Valence, Energy) in situations where the Dominance is unavailable or difficult to assess:
$$ \text{Distance} = \sqrt{(V_s - V_b)^2 + (E_s - E_b)^2} $$
Please note that the precision of the framework is reduced when using the approximate or 2D circumplex model.
The Technical Approach: Deconstructing Musical Features
For musicians, producers, or artificial intelligence models capable of detailed musical analysis, this third approach offers a “bottom-up” method. This technique involves deconstructing a song into its fundamental sonic components and matching that profile against the detailed characteristics defined for each emotion in the framework.
Deconstruct the Song. Analyse the track across the core musical characteristics used in the framework: Tempo (BPM), Rhythm (meter, groove), Harmony (key, chord progressions), Melody (contour, phrasing), Timbre (instrumentation, sound quality), Texture (density, layers), Dynamics (loudness variations), and Structure (form, arrangement).
Create a Sonic Profile. Document these characteristics objectively. For example, a profile for a typical electronic dance track might look like this:
- Tempo: Fast, ~128 BPM, stable.
- Rhythm: Propulsive 4/4 time, four-on-the-floor kick.
- Harmony: Major key, simple and powerful I-V-vi-IV progression.
- Timbre: Bright, stacked sawtooth-wave synths.
- Dynamics: Vast range, built around a long crescendo.
- Structure: Features an extended build-up section culminating in a drop.
Compare with the Framework. With this sonic profile, consult the framework’s database. Find the emotion whose “musical characteristics” most closely align with your analysis. In the example above, the profile is a near-perfect match for the characteristics listed for “SB-001, Euphoria / Ecstasy”.