Audio feature extraction and classification

In this work is proposed a speech emotion recognition model based on the extraction of four different features got from RAVDESS sound files and stacking the resulting matrices in a one-dimensional array by taking the mean values along the time axis. Then this array is fed into a 1-D CNN model as input.

The code shows implementations of basic features for voice/speech analysis.

A compact 8‑channel EEG method for depression screening is evaluated strictly at the subject level, revealing that true accuracy is much lower than segment‑level reports.

Lightweight and explainable speech-based stress detection using Temporal Energy Dynamics (TED), featuring energy-based features, SVM classification, and SHAP analysis.

This repository presents the results of a technological initiation that encouraged my undergraduate thesis, I sought to improve my knowledge in digital signal processing applied to music. It is a spectral analysis tool for the audio of musical instruments, focusing on objectively characterizing timbre.

Multi-input DenseNet architecture with data augmentation (sliding window/overlap). Published in IEIE (2022).

Streamlit app for frequency-domain audio signal analysis — FFT, spectrogram, spectral features (centroid, bandwidth, SFM, SCF), formant detection, and F0 estimation via cepstrum.