Related Experiment Video
Updated: Jan 21, 2026

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
Published on: June 18, 2020
Highly spectrally undersampled vowels can be classified by machines without supervision
Thayabaran Kathiresan1, Dieter Maurer2, Volker Dellwo3
1Institute of Computational Linguistics, University of Zurich, Andreasstrasse 15, CH-8050, Zurich, Switzerlandthayabaran.kathiresan@uzh.ch.
Abstract:
An unsupervised automatic clustering algorithm (k-means) classified 1282 Mel frequency cepstral coefficient (MFCC) representations of isolated steady-state vowel utterances from eight standard German vowel categories with fo between 196 and 698 Hz. Experiment I obtained the number of MFCCs (1-20) in connection with the spectral bandwidth (2-20 kHz) at which performance peaked (five MFCCs at 4 kHz). In experiment II, classification performance with different ranges of fo revealed that ranges with fo > 500 Hz reduced classification performance but it remained well above chance. This shows that isolated steady state vowels with strongly undersampled spectra contain sufficient acoustic information to be classified automatically.
More Related Videos
Related Concept Videos
Classifying Matter by Composition
According to its composition, the matter can be classified into two broad categories — pure substances and mixtures.
A pure substance is a form of matter that has a constant composition throughout with uniform properties. For example, any sample of sucrose has the same composition and same physical properties, such as melting point, color, and sweetness, regardless of the source from which it is isolated.
A mixture is composed of two or...
Classifying Matter by State
How Data are Classified: Numerical Data
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
Machines
A free-body diagram of the...
How Data are Classified: Categorical Data
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
Machines: Problem Solving II

