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Automatic Generation of Music for Inducing Emotive Response
Brigham Young University - Computer Science |
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Note: This page currently taken verbatim from the "Methods" section of the paper. A more web-friendly version will be coming soon. In order to produce selections with specifc emotional content, a separate set of musical selections is compiled for each desired emotion. Initial experiments focus on the six basic emotions outlined by Parrot[1]--love, joy, surprise, anger, sadness, and fear--creating a data set representative of each. Selections for the training corpora are taken from movie soundtracks due to the wide emotional range present in this genre of music. MIDIs used in the experiments can be found at the Free MIDI File Database. These MIDIs were rated by a group of research subjects. Each selection was rated by at least six subjects, and selections rated by over 80% of subjects as representative of a given emotion were then selected for use in the training corpora. Next, the system analyzes the selections to create statistical models of the data in the six corpora. Selections are first transposed into the same key. Melodies are then analyzed and n-gram models are generated representing what notes are most likely to follow a given series of notes in a given corpus. Statistics describing the probability of a melody note given a chord, and the probability of a chord given the previous chord, are collected for each of the six corpora. Information is also gathered about the rhythms, the accompaniment patterns, and the instrumentation present in the songs. Since not every melody produced is likely to be particularly remarkable, the system also makes use of multilayer perceptrons with a single hidden layer to evaluate the generated selections. Inputs to these neural networks are the default features extracted by the "Phrase Analysis" component of the freely available jMusic software. This component returns a vector of twenty-one statistics describing a given melody, including factors such as number of consecutive identical pitches, number of distinct rhythmic values, tonal deviation, and keycenteredness. A separate set of two networks is developed to evaluate both generated rhythms and generated pitches. The first network in each set is trained using analyzed selections in the target corpus as positive training instances and analyzed selections from the other corpora as negative instances. This is intended to help the system distinguish selections containing the desired emotion. The second network in each set is trained with melodies from all corpora versus melodies previously generated by the algorithm. In this way, the system learns to emulate melodies which have already been accepted by human audiences. Once the training corpora are set and analyzed, the system employs four different components: a Rhythm Generator, a Pitch Generator, a Chord Generator, and an Accompaniment and Instrumentation Planner. The functions of these components are explained in more detail in the following sections.  The rhythm for the selection with a desired emotional content is generated by selecting a phrase from a randomly chosen selection in the corresponding data set. The rhythmic phrase is then altered by selecting and modifying a random number of measures. The musical forms of all the selections in the corpus are analyzed, and a form for the new selection is drawn from a distribution representing these forms. For example, a very simple AAAA form, where each of four successive phrases contains notes with the same rhythm values, tends to be very common. Each new rhythmic phrase is analyzed by jMusic and then provided as input to the neural network rhythm evaluators. Generated phrases are only accepted if they are classifed positively by both neural networks. Pitch Generator Once the rhythm is determined, pitches are selected for the melodic line. These pitches are drawn according to the n-gram model constructed from the melody lines of the corpus with the desired emotion. A melody is initialized with a series of random notes, selected from a distribution that model which notes are most likely to begin musical selections in the given corpus. Additional notes in the melodic sequence are randomly selected based on a probability distribution of what note is most likely to follow the given series of n notes. The system generates several hundred possible series of pitches for each rhythmic phrase. As with the rhythmic component, features are then extracted from these melodies using jMusic and provided as inputs to the neural network pitch evaluators. Generated melodies are only selected if they are classifed positively by both neural networks. Chord Generator The underlying harmony is determined using a Hidden Markov Model, with pitches considered as observed events and the chord progression as the underlying state sequence. The Hidden Markov Model requires two conditional probability distributions: the probability of a melody note given a chord and the probability of a chord given the previous chord. The statistics for these probability distributions are gathered from the corpus of music representing the desired emotion. The system then calculates which set of chords is most likely given the melody notes and the two conditional probability distributions. Since many of the songs in the training corpora had only one chord present per measure, initial attempts at harmonization also make this assumption, considering only downbeats as observed events in the model.  The accompaniment patterns for each of the selections in the various corpora are categorized, and the accompaniment pattern for a generated selection is probabilistically selected from the patterns of the target corpus. Common accompaniment patterns included arpeggios, chords sounding on repeated rhythmic patterns, and a low base note followed by chords on non-downbeats. Instruments for the melody and harmonic accompaniment are also probabilistically selected based on the frequency of various melody and harmony instruments in the corpus. [1] Parrott, W.G.: Emotions in Social Psychology. Psychology Press, Philadelphia (2001) |