tf.raw_ops.Mfcc | TensorFlow v2.16.1 (original) (raw)
tf.raw_ops.Mfcc
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Transforms a spectrogram into a form that's useful for speech recognition.
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tf.raw_ops.Mfcc(
spectrogram,
sample_rate,
upper_frequency_limit=4000,
lower_frequency_limit=20,
filterbank_channel_count=40,
dct_coefficient_count=13,
name=None
)
Mel Frequency Cepstral Coefficients are a way of representing audio data that's been effective as an input feature for machine learning. They are created by taking the spectrum of a spectrogram (a 'cepstrum'), and discarding some of the higher frequencies that are less significant to the human ear. They have a long history in the speech recognition world, and https://en.wikipedia.org/wiki/Mel-frequency_cepstrumis a good resource to learn more.
| Args | |
|---|---|
| spectrogram | A Tensor of type float32. Typically produced by the Spectrogram op, with magnitude_squared set to true. |
| sample_rate | A Tensor of type int32. How many samples per second the source audio used. |
| upper_frequency_limit | An optional float. Defaults to 4000. The highest frequency to use when calculating the ceptstrum. |
| lower_frequency_limit | An optional float. Defaults to 20. The lowest frequency to use when calculating the ceptstrum. |
| filterbank_channel_count | An optional int. Defaults to 40. Resolution of the Mel bank used internally. |
| dct_coefficient_count | An optional int. Defaults to 13. How many output channels to produce per time slice. |
| name | A name for the operation (optional). |
| Returns |
|---|
| A Tensor of type float32. |
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Last updated 2024-04-26 UTC.