Thesis (MScEng (Electrical and Electronic Engineering))--Stellenbosch University, 2008. / A singing transcription system transforming acoustic input into MIDI note sequences
is presented. The transcription system is incorporated into a pronunciation-independent
sight-singing tutor system, which provides note-level feedback on the accuracy with which
each note in a sequence has been sung.
Notes are individually modeled with hidden Markov models (HMMs) using untuned
pitch and delta-pitch as feature vectors. A database consisting of annotated passages
sung by 26 soprano subjects was compiled for the development of the system, since no
existing data was available. Various techniques that allow efficient use of a limited dataset
are proposed and evaluated. Several HMM topologies are also compared, in analogy with
approaches often used in the field of automatic speech recognition. Context-independent
note models are evaluated first, followed by the use of explicit transition models to better
identify boundaries between notes. A non-repetitive grammar is used to reduce the
number of insertions. Context-dependent note models are then introduced, followed by
context-dependent transition models. The aim in introducing context-dependency is to
improve transition region modeling, which in turn should increase note transcription accuracy,
but also improve the time-alignment of the notes and the transition regions. The
final system is found to be able to transcribe sung passages with around 86% accuracy.
Finally, a note-level sight-singing tutor system based on the singing transcription system
is presented and a number of note sequence scoring approaches are evaluated.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/2687 |
| Date | 03 1900 |
| Creators | Krige, Willie |
| Contributors | Niesler, T. R., Stellenbosch University. Faculty of Engineering. Dept. of Electrical and Electronic Engineering. |
| Publisher | Stellenbosch : Stellenbosch University |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Rights | Stellenbosch University |
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