Difference between revisions of "GuitarFace"
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| − | + | == Motivation == | |
| − | + | As guitar players ourselves, we can appreciate a masterful guitar solo. Fast, dynamic solos comprise difficulty to some extent, but there's also "extra-musical" things like stage presence, emotion, and cool. | |
| + | Solving the difference between good and bad guitar solos is a monstrous task, but we believe that there are a number of MVPs in the same vein that would serve guitarists and other musicians well. A tool that tells you if you're playing on the beat, for example, is very useful for solo guitarists and other "bedroom musicians" who don't have that kind of feedback from a band, or perhaps can't even tell. Personal experience with lining up tracks in a DAW points to a need for excellent rhythm when recording and overdubbing. Other enhancements to the soloing experience could include knowing how close your sound is to Jimmy Page's, or transcribing a live improvisation of a song you want to remember later, or fine tuning your intonation and technique. | ||
| + | |||
| + | We choose rock music because of the spotlight that the guitar is frequently given in a rock band. Rock music is one of the largest genres of music, but there are individual artists who stand out as titans in the genre. Led Zeppelin, The Doors, and Jimi Hendrix are just a few examples of bands that truly convey masterfulness in the genre. | ||
| + | |||
| + | == The Product == | ||
| + | |||
| + | The product will use a support vector machine (SVM) to input MIDI data (tablature -> Guitar Pro -> MIDI) and feature vectors and output a classification. So, which features to include? | ||
| + | |||
| + | To give a quick example, rock and roll has 4 core instruments: the electric guitar, bass, drums, and voice. We could compare it to other genres on a basis of instrumentation, and find some differentiation. For example, jazz would have a noisier distribution of instruments: perhaps a peak over drums and the trumpet, but would we see the same over the piano and guitar? | ||
Similarly, there are other features besides instrumentation that are core to rock, and furthermore rock guitar soloing: | Similarly, there are other features besides instrumentation that are core to rock, and furthermore rock guitar soloing: | ||
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* creativity | * creativity | ||
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| + | == Design == | ||
| + | |||
| + | |||
| + | == Testing == | ||
| + | |||
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| + | == Team == | ||
| + | * Roshan Vidyashankar | ||
| + | * Gina Collecchia | ||
| + | |||
| + | == Milestones == | ||
| + | * Week 1: data acquisition and machine learning research | ||
| + | * Week 2: feature vector design, data scrubbing | ||
| + | * Week 3: interface design | ||
All of these features, when performed well or if they fall flat, can completely make or break a song. Naturally, we are interested in the measure of quality: what makes a good guitar solo? By "good", we mean retains our interest, impresses, and even inspires. | All of these features, when performed well or if they fall flat, can completely make or break a song. Naturally, we are interested in the measure of quality: what makes a good guitar solo? By "good", we mean retains our interest, impresses, and even inspires. | ||
Revision as of 14:07, 6 November 2013
By Roshan Vid and Gina Collecchia
Goals
To identify virtuosity in rock guitar solos by analyzing MIDI/Guitar Pro data with supervised learning (SVM).
Motivation
As guitar players ourselves, we can appreciate a masterful guitar solo. Fast, dynamic solos comprise difficulty to some extent, but there's also "extra-musical" things like stage presence, emotion, and cool.
Solving the difference between good and bad guitar solos is a monstrous task, but we believe that there are a number of MVPs in the same vein that would serve guitarists and other musicians well. A tool that tells you if you're playing on the beat, for example, is very useful for solo guitarists and other "bedroom musicians" who don't have that kind of feedback from a band, or perhaps can't even tell. Personal experience with lining up tracks in a DAW points to a need for excellent rhythm when recording and overdubbing. Other enhancements to the soloing experience could include knowing how close your sound is to Jimmy Page's, or transcribing a live improvisation of a song you want to remember later, or fine tuning your intonation and technique.
We choose rock music because of the spotlight that the guitar is frequently given in a rock band. Rock music is one of the largest genres of music, but there are individual artists who stand out as titans in the genre. Led Zeppelin, The Doors, and Jimi Hendrix are just a few examples of bands that truly convey masterfulness in the genre.
The Product
The product will use a support vector machine (SVM) to input MIDI data (tablature -> Guitar Pro -> MIDI) and feature vectors and output a classification. So, which features to include?
To give a quick example, rock and roll has 4 core instruments: the electric guitar, bass, drums, and voice. We could compare it to other genres on a basis of instrumentation, and find some differentiation. For example, jazz would have a noisier distribution of instruments: perhaps a peak over drums and the trumpet, but would we see the same over the piano and guitar?
Similarly, there are other features besides instrumentation that are core to rock, and furthermore rock guitar soloing:
- scale / key / intonation
- timing
- dynamic range (loudness)
- pitch range
- repetitiveness
- non-pitched decorations
- vibrato, bend
- stage presence (physical movements, facial expression)
- use of pedals / FX
- music theory of the context, expectation (build-up and violation)
- creativity
Design
Testing
Team
- Roshan Vidyashankar
- Gina Collecchia
Milestones
- Week 1: data acquisition and machine learning research
- Week 2: feature vector design, data scrubbing
- Week 3: interface design
All of these features, when performed well or if they fall flat, can completely make or break a song. Naturally, we are interested in the measure of quality: what makes a good guitar solo? By "good", we mean retains our interest, impresses, and even inspires.
We want to minimize the amount of subjectivity in our definition of quality, so
Since making general statements about style are subjective and difficult, we wondered if just a few or even one of these vectors could provide enough information to differentiate between good and bad guitar soloing. The pitch content (melody) of the solo is a convincing example, knowing what we do about major and minor keys and other scales. Timing is another: in general, things should fall on the beat or integer divisions thereof. We could also record other events, but something like the use of vibrato or tremolo is harder to project onto an axis of quality; it could be a threshold, or even time-dependent.
Violating these are clear violations of our expectations.
Ultimately, we hope to implement our model in the form of a game. It makes sense that actually playing the game should inform us further, since our players will be providing quality data. The game would provide feedback for your soloing, by rewarding good solos and punishing bad ones. If played by 2 people, this could send messages from a partner's superior solo to the opponents in the form of damage or further musical obstacles. The opponents could be dueling asynchronously, or essentially jamming together.
Pitch detection from just listening to
