The 'fastai' <https://docs.fast.ai/index.html> library
simplifies training fast and accurate neural networks
using modern best practices. It is based on research
in to deep learning best practices undertaken
at 'fast.ai', including 'out of the box' support
for vision, text, tabular, audio, time series, and
collaborative filtering models.

Interface to 'fastai'

package-ubuntu-20.04-r-4.0.5#cran#fastai-2.0.7

BasicMFCC(
  sample_rate = 16000,
  n_mfcc = 40,
  dct_type = 2,
  norm = "ortho",
  log_mels = FALSE,
  melkwargs = NULL
)

<table summary="R argblock">
<tr valign="top">
<td><code>sample_rate</code></td>
<td>
sample rate
</td>
</tr>
<tr valign="top">
<td><code>n_mfcc</code></td>
<td>
number of mel-frequency cepstral coefficients
</td>
</tr>
<tr valign="top">
<td><code>dct_type</code></td>
<td>
dct type
</td>
</tr>
<tr valign="top">
<td><code>norm</code></td>
<td>
normalization type
</td>
</tr>
<tr valign="top">
<td><code>log_mels</code></td>
<td>
apply log to mels
</td>
</tr>
<tr valign="top">
<td><code>melkwargs</code></td>
<td>
additional arguments for mels
</td>
</tr>
</table>

package-ubuntu-20.04-r-4.0.5#cran#fastai-2.0.7#docs#BasicMFCC

BasicMFCC

Description

Usage

Arguments

Value

fastai

We don't support your browser anymore

`sample_rate`	sample rate
`n_mfcc`	number of mel-frequency cepstral coefficients
`dct_type`	dct type
`norm`	normalization type
`log_mels`	apply log to mels
`melkwargs`	additional arguments for mels