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Estimating Tonal Tension from Audio Content
| Title | Estimating Tonal Tension from Audio Content |
| Publication Type | Miscellaneous |
| Year of Publication | 2009 |
| Authors | Laurier, C. |
| Abstract |
Background
Detecting emotions from music based on audio content is a relatively recent problem. Most of the approaches tend to categorize the whole musical piece and only a few focus on the time evolution of the emotion.
Aims
In this study, we investigate one particular emotional aspect: tension. Moreover, we narrow our work to model a particular component of it: the tonal tension.
Methods
We extract tonal information from the audio content in the form of Harmonic Pitch Class Profiles (HPCPs). Then, from this sequence of HPCP vectors, we compute the distances one by one based on Harte's distance (projection of the 12 tones in a 6-D space and euclidean distance). We also extract informations from this curve computing the mean, variance and other statistical measures.
Results
We find correlations comparing the tonal tension model from Lerdahl and the tension curves we extract from the audio content. We also find correlations between statistical descriptions of the tension curves and tension ratings from listeners.
Conclusions
This technique offers promising results and a simplistic model of tonal tension. Future works will consist in enhancing this model to get a more accurate representation of the tonal tension.
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