Your English writing platform
Discover LudwigSuggestions(1)
Exact(1)
In this paper, we will use the separable compressive sensing and singular value decomposition (SVD) to detect the throat polyps based on support vector machine (SVM) algorithm while reducing the burden of voice data collection and storage.
Similar(59)
Mobility segment consists of voice, data, and home monitoring activities.
Time-series of voice data are nonstationary, with their statistical characteristics change over time when spoken.
Furthermore the segment lengths can be variables that cope with the level of fluctuation of the voice data, dynamically.
Due to the burden of voice signals in storage and computation, we used the separable compressive sensing theory for data compressing and sampling.
Submarine cables are thus constituting the backbone of the voice, data, and Internet international network.
In their study, 32.2% of the voice data were classified into three types with 79.8% accuracy.
Only the cepstrum coefficients are used as the encoding result of time-series voice data.
It is the reason that LPC is chosen here for the purpose of encoding the voice data.
Raw data are often dirty, and this places the burden of potentially expensive data cleaning on the data provider.
Twenty-three variables computed by the commercial "Dr.Speech" software from a digital voice recording of a sustained phonation of the vowel sound/a/constitute a voice data vector.
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com