Technology is the most important
To create a product that is really able to affect the parenthood of millions of families very important quality technology tested by time. Our team for many years strive to be market leaders through own unique developments
Interpreting an infant's emotional state based on their cry is critical in both medical and everyday contexts. Crying is the primary form of communication for infants and often contains bio-signals that reflect their needs and well-being. Our system applies a comprehensive approach to analyze these signals using multi-domain feature extraction, time-series visualization, and hierarchical classification powered by machine learning models.
Introduction
Our solution consists of a modular pipeline:
- Acoustic segmentation — identifying cry episodes
- Digital signal preprocessing — normalization, filtering, and background noise reduction
- Multi-level feature extraction:
- ZCR, RMS — time-domain dynamics
- Mel-spectrogram, MFCC — spectral and cepstral features
- TSI (Time Series Imaging) — converting MFCC into image representations
- Machine learning classification — predicting the cry cause
System Architecture
Audio samples are recorded at 22,050 Hz and 5 seconds in duration. Preprocessing includes:
- Pre-emphasis filtering
- Windowing
- Normalization
- Amplitude equalization
Audio Signal Processing
- ZCR (Zero Crossing Rate): rate of sign changes in the signal
- RMS (Root Mean Square): measures the energy intensity of the waveform
- Mel-Spectrogram: frequency representation scaled to human auditory perception
- MFCC: extracted using DFT → Mel filterbank → log → DCT to simulate auditory encoding
Time-Domain & Frequency Features
To enhance classification accuracy, MFCC vectors are transformed into images:
These images are used to train machine vision models.
- GADF (Gramian Angular Difference Fields)
- RP (Recurrence Plot)
- MTF (Markov Transition Fields)
These images are used to train machine vision models.
Time-Series Imaging (TSI)
Results and Metrics
- Model ensemble: - Random Forest - Support Vector Machine (SVM) - K-Nearest Neighbors (KNN) - BaggingClassifier
- Dataset: - 15,000+ labeled cry recordings (hunger, discomfort, belly pain, burping, tiredness) - Cross- validation (K=10), grid search, and data augmentation (noise, stretch, amplitude)
Classification & Training
Our interface is designed with parents in mind. The app provides clear feedback in the form of simple messages such as:
- "The baby is likely hungry"
- "The baby may be uncomfortable"
- "The baby has belly pain"
The interface is built for clarity and ease of use.
- "The baby is likely hungry"
- "The baby may be uncomfortable"
- "The baby has belly pain"
The interface is built for clarity and ease of use.
User Interface and UX
- Speech development monitoring
- Predictive diagnostics (autism, hearing issues)
- Wearable integration (temp, HRV, motion)
Future Potential
Our system integrates audio signal processing, time-series imaging, computer vision, and machine learning to interpret baby cries. It provides real-time insight for parents and holds future promise for pediatric diagnostics.
Conclusion
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Baby Cry Analyzer
Baby Cry Analyzer
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