MFCC Features from 646 Short Speech Audio Samples

Comprising Mel-frequency cepstral coefficient (MFCC) features extracted from 646 short speech audio clips. It was created by Parmann and last updated in March 2022.

Use Cases

• Train a classifier on MFCC features to categorize short speech audio clips.
• Analyze the distribution of MFCC feature vectors across the 646 audio samples.
• Use the extracted MFCC features as input for speech signal processing or audio pattern recognition models.

Strengths

• Contains 646 distinct audio samples for analysis.
• Features are pre-extracted MFCCs, a standard representation for audio analysis.
• Dataset is tagged for use with common libraries like pandas and polars.

Limitations

• Small sample size of 646 audio clips may limit model generalization.
• No column descriptions or sample data are available to understand the feature dimensions.
• The dataset's specific speech content, speaker demographics, and recording conditions are unknown.

Provenance

Source

Hugging Face, author Parmann.

Collection Method

MFCC features were extracted from short speech audio files.

Freshness

Last updated on 2022-03-13.

Geography

Tagged with Region 'us', but specific coverage is unknown.