Re: Chord Scale Generator 1.3 Keygen 14 Jackson Couleur Fina
Emmanuelle Riker
Thanks for writing! I think you are referring to the rock with you chord sequence? The next chord there is a Bb/C, which is the same shape as the final slash chord up one fret, giving that dominant feel. I hope that answers your question.
Medieval church music was based on one of eight scales or modes. Certain of the modes were used for joyful music, others for meditative chant and still others to tell sad stories. All of these modes were built from the notes in the C major scale (white keys on the piano). For example, the first mode was D, E, F, G, A, B, C, D. The third mode began on E and used only the naturals: E, F, G, A, B, C, D, E. The fifth mode went from F to F, and the seventh mode from G to G. These odd numbered modes were called the authentic modes. Created from each of the authentic modes was a plagal mode. The plagal mode was related to the authentic mode in that it used the same notes and ended on the same "finalis" (final note), but the range of the melody was different. (Click here to find out why chanting rather than singing or speaking)
Jazz players use the dorian scales to improvise on minor seventh chords when they are built on the second note of the scale. Example: dm7 in the key of C - use the d dorian scale. If you outline the chord, you can see that the d dorian scale fits right into it and the notes in between become passing tones!
Jazz players use the mixolydian scales to improvise on dominant seventh chords. Example: G7 - use the G mixolydian scale. If you outline the chord, you can see that the G mixolydian scale fits right into it!
In [34], it was proven that there exists a correlation between the probability of the generated chord sequences and the complexity perceived by the listeners. In order to create the dataset used for training the VAEs and generating the complexity-dependent latent space, it is first necessary to describe how it is possible to sample chord sequences using the compound language model proposed in [34]. Normally, the procedure would be to sample one chord at a time, that is extracting \(x_i\) from \(p(xx_0^i)\), however, using such a technique, it would be impossible to control the final probability of the whole sequence. We instead follow what was done in [55] and consider a dataset sampled using a combination of temperature sampling and uniform sampling.
where \(\tau\) is the temperature parameter and \(\mathcal X\) is the set of possible chords. Different \(\tau\) values cause different effects. While \(\tau =1\) maintains the original probabilities, \(\tau \rightarrow \infty\) tends to make the distribution uniform, and finally, values \(\tau \rightarrow 0\) tend to output the most probable chords.
Before performing the actual listening test, the participants were profiled according to their musical background, through the self-report questionnaire of the Goldsmiths Musical Sophistication Index (GMSI) [62]. The test consists of 38 questions with seven-point scale answers for each question. The answers are then combined to form 5 sub-factors (active engagement, perceptual abilities, musical training, singing abilities, emotions) and finally one general factor (general music sophistication factor). In Fig. 9 we show a histogram plot representing the GMSI of the participants.
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