Transforming high-effort voices into breathy voices using adaptive pre-emphasis linear prediction

dc.contributor.authorNordstrom, Karl
dc.contributor.supervisorDriessen, Peter of Electrical and Computer Engineeringen_US of Philosophy Ph.D.en_US
dc.description.abstractDuring musical performance and recording, there are a variety of techniques and electronic effects available to transform the singing voice. The particular effect examined in this dissertation is breathiness, where artificial noise is added to a voice to simulate aspiration noise. The typical problem with this effect is that artificial noise does not effectively blend into voices that exhibit high vocal effort. The existing breathy effect does not reduce the perceived effort; breathy voices exhibit low effort. A typical approach to synthesizing breathiness is to separate the voice into a filter representing the vocal tract and a source representing the excitation of the vocal folds. Artificial noise is added to the source to simulate aspiration noise. The modified source is then fed through the vocal tract filter to synthesize a new voice. The resulting voice sounds like the original voice plus noise. Listening experiments were carried out. These listening experiments demonstrated that constant pre-emphasis linear prediction (LP) results in an estimated vocal tract filter that retains the perception of vocal effort. It was hypothesized that reducing the perception of vocal effort in the estimated vocal tract filter may improve the breathy effect. This dissertation presents adaptive pre-emphasis LP (APLP) as a technique to more appropriately model the spectral envelope of the voice. The APLP algorithm results in a more consistent vocal tract filter and an estimated voice source that varies more appropriately with changes in vocal effort. This dissertation describes how APLP estimates a spectral emphasis filter that can transform the spectral envelope of the voice, thereby reducing the perception of vocal effort. A listening experiment was carried out to determine whether APLP is able to transform high effort voices into breathy voices more effectively than constant pre-emphasis LP. The experiment demonstrates that APLP is able to reduce the perceived effort in the voice. In addition, the voices transformed using APLP sound less artificial than the same voices transformed using constant pre-emphasis LP. This indicates that APLP is able to more effectively transform high-effort voices into breathy voices.en_US
dc.identifier.bibliographicCitationK. I. Nordstrom, G. Tzanetakis and P. F. Driessen, “Transforming high-effort voices into breathy voices using adaptive pre-emphasis linear prediction“, IEEE Transactions on Audio, Speech and Language Processing (accepted for publication).en_US
dc.identifier.bibliographicCitationK. I. Nordstrom and P. F. Driessen, “Variable preemphasis LPC for modeling vocal effort in the singing voice“, Proceedings of the 9th International Conference on Digital Audio Effects (DAFx06), Montreal, QC, Canada, September 2006.en_US
dc.identifier.bibliographicCitationK. I. Nordstrom, P. F. Driessen, and G. A. Rutledge, “Influence of the LPC filter upon the perception of breathiness and vocal effort“, IEEE Int. Symposium on Signal Processing and Information Technology (ISSPIT06), Vancouver, BC, Canada, August 2006.en_US
dc.identifier.bibliographicCitationK. I. Nordstrom, G. A. Rutledge, P. F. Driessen, “Using voice conversion as a paradigm for analyzing breath quality“, IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PacRim05), Victoria, BC, Canada, August, 2005.en_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectvoice transformationen_US
dc.subjectvoice modelingen_US
dc.subjectlinear predictionen_US
dc.subjectadaptive pre-emphasisen_US
dc.subjectvoice qualityen_US
dc.subjectvocal tract filteren_US
dc.subjectformant filteren_US
dc.subjectvoice sourceen_US
dc.subjectglottal sourceen_US
dc.subject.lcshUVic Subject Index::Sciences and Engineering::Engineering::Electrical engineeringen_US
dc.titleTransforming high-effort voices into breathy voices using adaptive pre-emphasis linear predictionen_US


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