🔉 spafe: Simplified Python Audio Features Extraction
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Updated
May 30, 2024 - Python
🔉 spafe: Simplified Python Audio Features Extraction
A suite of speech signal processing tools
XSpeech: A Novel Deep Learning Approach to Classifying Stutters
A library for audio and music analysis, feature extraction.
A differentiable version of SPTK
Speaker Recognition deep learning model based on feature extraction from Mel Frequency Cepstral Coefficients. Solution code for Signal Processing Cup 2024.
Speaker Recognition deep learning model based on feature extraction from Mel Frequency Cepstral Coefficients
Kaldi-compatible online & offline feature extraction with PyTorch, supporting CUDA, batch processing, chunk processing, and autograd - Provide C++ & Python API
Speech Emotion Recognition (SER) using CNNs and CRNNs Based on Mel Spectrograms and Mel Frequency Cepstral Coefficients (MFCCs)
This project was originally developed for my own company, Delta Cognition, and later applied during my 2023 internship. It is a text-independent speaker recognition solution utilizing machine learning techniques.
Music genre Classification with different models fine tuning and performance comparisons
In this project we used TESS voice dataset and processed it and perform emotion prediction.
Project for classifying audio files into different genres using the K-Nearest Neighbors (KNN) algorithm.
Constant-Q harmonic coefficients (CQHCs), a timbre feature designed for music signals.
Zafar's Audio Functions in Julia for audio signal analysis: STFT, inverse STFT, CQT kernel, CQT spectrogram, CQT chromagram, MFCC, DCT, DST, MDCT, inverse MDCT.
Zafar's Audio Functions in Matlab for audio signal analysis: STFT, inverse STFT, mel filterbank, mel spectrogram, MFCC, CQT kernel, CQT spectrogram, CQT chromagram, DCT, DST, MDCT, inverse MDCT.
Zafar's Audio Functions in Python for audio signal analysis: STFT, inverse STFT, mel filterbank, mel spectrogram, MFCC, CQT kernel, CQT spectrogram, CQT chromagram, DCT, DST, MDCT, inverse MDCT.
Implemented CNN and LSTM models in TensorFlow for classifying bird sounds across 10 species.
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