Title:
N-gram modeling of tabla sequences using Variable-Length Hidden Markov Models for improvisation and composition
N-gram modeling of tabla sequences using Variable-Length Hidden Markov Models for improvisation and composition
| dc.contributor.advisor | Weinberg, Gil | |
| dc.contributor.author | Sastry, Avinash | en_US |
| dc.contributor.committeeMember | Fiebrink, Rebecca | |
| dc.contributor.committeeMember | Freeman, Jason | |
| dc.contributor.department | Music | en_US |
| dc.date.accessioned | 2012-02-17T19:20:25Z | |
| dc.date.available | 2012-02-17T19:20:25Z | |
| dc.date.issued | 2011-09-20 | en_US |
| dc.description.abstract | This work presents a novel approach for the design of a predictive model of music that can be used to analyze and generate musical material that is highly context dependent. The system is based on an approach known as n-gram modeling, often used in language processing and speech recognition algorithms, implemented initially upon a framework of Variable-Length Markov Models (VLMMs) and then extended to Variable-Length Hidden Markov Models (VLHMMs). The system brings together various principles like escape probabilities, smoothing schemes and uses multiple representations of the data stream to construct a multiple viewpoints system that enables it to draw complex relationships between the different input n-grams, and use this information to provide a stronger prediction scheme. It is implemented as a MAX/MSP external in C++ and is intended to be a predictive framework that can be used to create generative music systems and educational and compositional tools for music. A formal quantitative evaluation scheme based on entropy of the predictions is used to evaluate the model in sequence prediction tasks on a database of tabla compositions. The results show good model performance for both the VLMM and the VLHMM while highlighting the expensive computational cost of higher-order VLHMMs. | en_US |
| dc.description.degree | MS | en_US |
| dc.identifier.uri | http://hdl.handle.net/1853/42792 | |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.subject | Musical sequence modeling | en_US |
| dc.subject | Markov models | en_US |
| dc.subject | Hidden markov models | en_US |
| dc.subject | N-gram modeling | en_US |
| dc.subject | Tabla | en_US |
| dc.subject | North Indian classical music | en_US |
| dc.subject | Machine learning | en_US |
| dc.subject | Music model | en_US |
| dc.subject.lcsh | Markov processes | |
| dc.subject.lcsh | Improvisation (Music) | |
| dc.subject.lcsh | Music | |
| dc.title | N-gram modeling of tabla sequences using Variable-Length Hidden Markov Models for improvisation and composition | en_US |
| dc.type | Text | |
| dc.type.genre | Thesis | |
| dspace.entity.type | Publication | |
| local.contributor.advisor | Weinberg, Gil | |
| local.contributor.corporatename | College of Design | |
| local.contributor.corporatename | School of Music | |
| local.relation.ispartofseries | Master of Science in Music Technology | |
| relation.isAdvisorOfPublication | f3feda3b-c805-4675-842e-01a40f8b40a4 | |
| relation.isOrgUnitOfPublication | c997b6a0-7e87-4a6f-b6fc-932d776ba8d0 | |
| relation.isOrgUnitOfPublication | 92d2daaa-80f2-4d99-b464-ab7c1125fc55 | |
| relation.isSeriesOfPublication | bb52c603-2646-4dfa-a9b7-9f81b43c419a |
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