Expressive musical performing style involves more than what is simply represented on the score. Performers imprint their personal style on each performances based on their musical understanding. Expressive musical performing style makes the music come alive by shaping the music through continuous variation. It is observed that the musical style can be represented by appropriate numerical parameters, where most parameters are related to the dynamics. It is also observed that performers tends to perform music sections and motives of similar shape in similar ways, where music sections and motives can be identified by an automatic phrasing algorithm. An experiment is proposed for producing expressive music from raw quantized music files using machine-learning methods like Support Vector Machines. Experimental results show that it is possible to induce some of a performer’s style by using the music parameters extracted from the audio recordings of their real performance.

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The traditional way of emotional classification involves using the two-dimensional (2D) emotional model by Thayer, which identifies emotion by arousal and valence. The 2D model is not fine enough to classify among the rich vocabularies of emotions, such as distinguish between disgusting and fear. Another problem of the traditional methods is that they don’t have a formal definition of the axis value of the emotional model. They either assign the axis value manually or rate them by listening test. We propose to use the PAD (Pleasure, Arousal, Dominance) emotional state model to describe speech emotion in a continuous 3-dimensional scale. We suggest an initial definition of the continuous axis values by observing into the pattern of Log Frequency Power Coefficients (LFPC) fluctuation. We verify the result using a database of German emotional speech. Experiments show that the classification result of a set of big-6 emotions on average is 81%.

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