Pop music automation is a field of study among musicians and computer scientists with a goal of producing successfulpop music algorithmically. It is often based on the premise that pop music is especially formulaic, unchanging, and easy to compose. The idea of automating popmusic composition is related to many ideas inalgorithmic music,artificial intelligence (AI) andcomputational creativity.
Algorithms (or, at the very least, formal sets of rules) have been used to compose music for centuries; the procedures used to plot voice-leading incounterpoint, for example, can often be reduced to algorithmic determinant. Now the term is usually reserved, however, for the use of formal procedures to make music without human intervention.
Classical music automation software exists that generates music in the style ofMozart andBach andjazz. Most notably,David Cope[1] has written a software system called "Experiments in Musical Intelligence" (or "EMI") that is capable of analyzing and generalizing from existing music by a human composer to generate novel musical compositions in the same style. EMI's output is convincing enough to persuade human listeners that its music is human-generated to a high level of competence.
Creativity research in jazz has focused on the process ofimprovisation and the cognitive demands that this places on a musical agent: reasoning about time, remembering and conceptualizing what has already been played, and planning ahead for what might be played next.
Inevitably associated with pop music automation ispop music analysis.
Projects in pop music automation may include, but are not limited to, ideas in melody creation and song development, vocal generation or improvement, automatic accompaniment and lyric composition.
Some systems exist that automatically choose chords to accompany a vocal melody in real-time. A user with no musical experience can create a song with instrumental accompaniment just by singing into a microphone.An example is a Microsoft Research project called Songsmith,[2] which trains aHidden Markov model using a music database and uses thatmodel to select chords for new melodies.
Automatic melody generation is often done with aMarkov chain, the states of the system become note or pitch values, and aprobability vector for each note is constructed, completing a transition probability matrix (see below). An algorithm is constructed to produce an output note values based on the transition matrix weightings, which could beMIDI note values, frequency (Hz), or any other desirable metric.
| Note | A | C# | Eb |
|---|---|---|---|
| A | 0.1 | 0.6 | 0.3 |
| C# | 0.25 | 0.05 | 0.7 |
| Eb | 0.7 | 0.3 | 0 |
| Note | A | D | G |
|---|---|---|---|
| AA | 0.18 | 0.6 | 0.22 |
| AD | 0.5 | 0.5 | 0 |
| AG | 0.15 | 0.75 | 0.1 |
| DD | 0 | 0 | 1 |
| DA | 0.25 | 0 | 0.75 |
| DG | 0.9 | 0.1 | 0 |
| GG | 0.4 | 0.4 | 0.2 |
| GA | 0.5 | 0.25 | 0.25 |
| GD | 1 | 0 | 0 |
A second-order Markov chain can be introduced by considering the current stateand also the previous state, as indicated in the second table. Higher,nth-order chains tend to "group" particular notes together, while 'breaking off' into other patterns and sequences occasionally. These higher-order chains tend to generate results with a sense ofphrasal structure, rather than the 'aimless wandering' produced by a first-order system.[3]
Automated lyric creating software may take forms such as:
The Tra-la-Lyrics system[4] produces song lyrics, in Portuguese, for a given melody. This not only involves matching each word syllable with a note in the melody, but also matching the word's stress with the strong beats of the melody.
This involvesnatural language processing. Pablo Gervás[5] has developed a noteworthy system called ASPERA that employs acase-based reasoning (CBR) approach to generating poetic formulations of a given input text via a composition of poetic fragments that are retrieved from a case-base of existing poems. Each poem fragment in the ASPERA case-base is annotated with a prose string that expresses the meaning of the fragment, and this prose string is used as the retrieval key for each fragment.Metrical rules are then used to combine these fragments into a well-formed poetic structure.
Programs likeTALE-SPIN[6] and The MINSTREL[7] system represent a complex elaboration of this basis approach, distinguishing a range of character-level goals in the story from a range of author-level goals for the story. Systems like Bringsjord's BRUTUS[8] can create stories with complex interpersonal themes like betrayal. On-line metaphor generation systems like 'Sardonicus' or 'Aristotle'[9] can suggest lexical metaphors for a given descriptive goal (e.g., to describe a supermodel as skinny, the source terms “pencil”, “whip”, “whippet”, “rope”, “stick-insect” and “snake” are suggested).
Using a language database (such aswordnet) one can create musings on a subject that may be weak grammatically but are still sensical. See such projects as the Flowerewolf automatic poetry generator or the Dada engine.
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