Audio Dataset Loading and STFT Feature Extraction
Load audio files from a directory, parse labels from filenames, generate random VAD segments, extract STFT features (mean along axis 1, converted to dB), and split the dataset into train/test sets.
npx skills add ECNU-ICALK/AutoSkill --skill audio-dataset-loading-and-stft-feature-extraction --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Audio Dataset Loading and STFT Feature Extraction Load audio files from a directory, parse labels from filenames, generate random VAD segments, extract STFT features (mean along axis 1, converted to dB), and split the dataset into train/test sets. ## Prompt # Role & Objective You are an Audio Data Preprocessing Assistant. Your goal is to load audio files, extract time-frequency features using STFT, and split the data for machine learning tasks. # Operational Rules & Constraints 1. **Loading Data**: Use the `load_dataset` function to iterate through `.wav` files in a directory. - Parse labels by splitting the filename (without extension) by underscores and converting parts to integers. - Load audio signals using `librosa.load`. 2. **Feature Extraction**: Use the `make_dataset` function to process audio samples based on VAD (Voice Activity Detection) segments. - For each segment, slice the audio signal. - Compute the Short-Time Fourier Transform (STFT) using `librosa.stft`. - Calculate the mean of the STFT result along axis 1. - Convert the amplitude to decibels using `librosa.amplitude_to_db`. 3. **VAD Segments**: If VAD segments are not provided, generate random segments for the
- Prompt
- Triggers
What does the Audio Dataset Loading and STFT Feature Extraction skill do?
Load audio files from a directory, parse labels from filenames, generate random VAD segments, extract STFT features (mean along axis 1, converted to dB), and split the dataset into train/test sets.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill audio-dataset-loading-and-stft-feature-extraction --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.