10 Practical openSMILE Recipes for Speech and Emotion Analysis

Advanced openSMILE Configurations: Custom Features and Pipelines

Overview

openSMILE is a flexible toolkit for extracting audio features (low-level descriptors and functionals) from speech and other audio. Advanced configurations let you create custom feature sets, process streams in real time, and build multi-stage pipelines that combine preprocessing, feature extraction, selection, and export for machine learning.

Key Concepts

• Config files: openSMILE’s behavior is driven by INI-style config files (.conf) that declare components (frames, windows, feature calculators, aggregators) and their connections.
• Component types: frameCutter, windower, spectral analyzers, LLD (low-level descriptor) extractors, functionals (statistical aggregators), and sinks (CSV, ARFF, network).
• Objects and names: modules are instances of classes; connect them via names (instance/component parameters).
• Real-time vs. batch: real-time uses streaming components, ringbuffers, and non-blocking sinks; batch can use longer windows and global functionals.

Example goals (choose one for implementation)

1. Custom LLD set focused on prosody and voice quality (F0, jitter, shimmer, HNR, RMS energy, spectral tilt).
2. Multi-resolution pipeline: short-term LLDs (10–25 ms) + mid-term features (200–1000 ms) + long-term functionals per file.
3. Real-time low-latency extractor sending features over network (OSC/TCP) to a downstream ML service.
4. Feature fusion pipeline: audio + derived linguistic timestamps (ASR) merged into a single feature stream.

Practical configuration steps

1. Start from a base config: copy opensmile/config/IS09 or emobase/egemaps configs as templates.
2. Define frame/windower settings: set FrameSize and FrameStep for short-term LLDs; add a second windower module for mid-term features.
3. Select/remove feature calculators: enable calculators for desired LLDs (e.g., F0, energy, spectral moments); disable unused ones to reduce CPU/memory.
4. Add custom calculators: implement new feature extractors by extending the C++ framework (SMILEComponent) or use the existing “cVectorProcessor”/“cFunctional” blocks to compute combinations.
5. Configure functionals: set which statistics (mean, std, percentiles, regression slope) to compute per segment/file.
6. Set sinks and formats: enable CSV/ARFF for batch, or enable cDataSocket/cTcpClient for streaming. Use header options for consistent ML pipelines.
7. Optimize performance: reduce buffer sizes for latency-sensitive setups, compile with optimization flags, or use fewer features.
8. Version control configs: keep configs in a repo and document parameter values for reproducibility.

Example snippets

• Short-term frame settings (conceptual):

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frameSize = 0.025 frameStep = 0.010 

• Enabling a mid-term window (conceptual):

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midFrameSize = 0.5 midFrameStep = 0.25 

Real-time pipeline tips

• Use small frame steps and ringbuffers; ensure downstream ML can handle input rate.
• Optionally run endpointing/VAD to avoid processing silence.
• Send feature deltas to capture dynamics without full functionals.

Validation and debugging

• Use openSMILE’s verbose/logging options to trace component connections.
• Compare outputs to known configs (e.g., eGeMAPS) for sanity checks.
• Visualize time-series LLDs (e.g., in Python matplotlib) to inspect behavior.

Common pitfalls

• Mismatched units (Hz vs. semitones) — normalize where needed.
• Excessive functionals cause high-dimensional outputs — apply selection/PCA.
• Real-time network delays — monitor latency and packet loss.

Further reading and resources

• openSMILE config examples (eGeMAPS, IS09, ComParE)
• openSMILE source docs and component reference
• Community recipes for emotion and speaker tasks