Cry analyzer

Analyzes cry audio to extract features and classify vocalizations, delivering objective metrics for researchers, clinicians, and product teams.

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About This Tool

Cry Analyzer processes uploaded cry audio to extract acoustic features and provide cry-type likelihoods. It enables researchers, clinicians, and developers to obtain objective, comparable metrics from vocalizations.
The system relies on standard signal-processing techniques to transform raw audio into a structured feature vector, including pitch, loudness, spectral features, and MFCCs. A lightweight classifier maps features to cry-type categories with associated confidence.
Key differentiators include automated data standardization, batch processing, and export options, which support reproducible studies and product analytics. Use cases span infant development research, pediatric screening, and cry-sensitive product testing.

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How to Use

Provide inputs: Upload an audio file in wav or mp3 format.
Optionally attach metadata: subject_age and recording_conditions.
Initiate analysis to extract pitch, energy, MFCCs, spectral features, and cry-type likelihoods.
Review outputs: per-feature metrics and cry-type probabilities with confidence.
Export results as CSV or JSON for research or product development.

Frequently Asked Questions

Find Quick Answers

What does the tool analyze?

The tool analyzes cry audio to extract acoustic features such as pitch, loudness, MFCCs, and spectral entropy, and provides a cry-type likelihood vector that indicates the probability of each cry category.

Which audio formats are supported?

Supported formats include WAV, MP3, and CAF. The uploader accepts common pediatric cry recordings; extremely long files may be segmented for processing.

What outputs are produced?

The tool returns a feature vector with units (Hz, dB, etc.), aggregated statistics, and a cry-type probability distribution. Results can be exported as CSV or JSON for downstream analyses.

What are the limitations?

This is not a medical device. Ambient noise, recording conditions, and subject variability can affect accuracy. Use results for research and development insights, not clinical diagnosis; ensure proper privacy controls for handling pediatric data.

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