🔉 👦 👧 👩 👨 Speaker identification using voice MFCCs and GMM

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.

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.

Deep Learning model for lexical stress detection in spoken English

Functions for creating speech features in MATLAB.

Speech Emotion Recognition using Deep Learning

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.

Speech Recognition on Spoken Digit Dataset using Bidirectional LSTM Model in PyTorch.

CovidCoughNet: A new method based on convolutional neural networks and deep feature extraction using pitch-shifting data augmentation for covid-19 detection from cough, breath, and voice signals

Mel Frequency Cepstral Coefficients (MFCCs) are a feature widely used in automatic speech and speaker recognition. They were introduced by Davis and Mermelstein in the 1980s, and have been state-of-the-art ever since. In this project, we have implemented MFCC feature extraction in Matlab.

Statistical Methods in Artificial Intelligence course project on implementing the paper Music Genre Classification by Haggblade et al (http://cs229.stanford.edu/proj2011/HaggbladeHongKao-MusicGenreClassification.pdf)

A python script that implements an automatic speech recognision system.

Klasifikasi Musik Berdasarkan Genre Menggunakan Metode Naive Bayes.

Overall process of speech signal processing (Mel-spectrogram & MFCCs) and loading data using Pytorch dataloader

Streamline your ecoacoustic analysis with LEAVES, offering advanced tools for large-scale soundscape annotation and visualization. Join researchers and citizen scientists using LEAVES to analyze complex soundscapes faster and more accurately.

A tool for instrument recognition.

Prediction of Human Central Features Using a PLP-CNN Voice Input Approach

This project explores emotion recognition in audio data, focusing on feature extraction techniques while also comparing the performance of LSTM and 1D CNN models.

Source Code for the book Building Machine Learning Systems with Python

Using Mel-frequency cepstral coefficients (MFCCs) for feature extraction and deep learning model for prediction