mel-frequencies

There are 1 repository under mel-frequencies topic.

SuperKogito / Voice-based-gender-recognition
:sound: :boy: :girl:Voice based gender recognition using Mel-frequency cepstrum coefficients (MFCC) and Gaussian mixture models (GMM)
gender-recognition gender-detection gender-classification gmm mfcc gender-recognition-by-voice voice mel-frequencies gaussian-mixture-models signal machine-learning data-science vocal speech gender scikit-learn scikit-learn-python speaker
Language:Python 184
SuperKogito / Voice-based-speaker-identification
:sound: :boy: :girl: :woman: :man: Speaker identification using voice MFCCs and GMM
speaker-identification speaker-recognition gmm mfcc voice mel-frequency-cepstral-coefficients mel-frequencies gaussian-mixture-models signal machine-learning vocal speech scikit-learn scikit-learn-python
Language:Python 48
evanotero / deep-music-genre-classification
🎵 Using Deep Learning to Categorize Music as Time Progresses Through Spectrogram Analysis
cnn rnn lstm keras-tensorflow spectrogram mel-frequencies deep-learning music-information-retrieval genre-classification sequence-analysis unbalanced-data classification
Language:Python 22
Project-6 / Emotion-recognition
Bimodal emotion recognition through facial expressions and speech
emotion-recognition matlab speech-emotion-recognition facial-expression-recognition face-detection mel-frequencies
Language:MATLAB 9
nvmoyar / voice-user-interfaces--notes
Notebooks notes about Automatic Speech Recognition
hidden-markov-models acoustic-model cepstral fourier-transform mel-frequencies n-grams
Language:Jupyter Notebook 5
parthp7 / ASV-Spoof-Detection
Spoofing detection system for automatic speaker verification
signal-processing mel-frequencies spoofing feature-extraction
Language:Python 3
nehith23 / Speech-Sentiment-Analysis
Sentiment Analysis from Speech!
machine-learning python3 cnn-keras mel-frequencies melspectrogram
Language:Jupyter Notebook 1
sagardoy / Simple-Speech-Command-Algorithm
The purpose of this project was to build an algorithm that can understand a small library of 10 simple commands while being able to ignore, or distinguish from, unknown commands. I submitted this work for the Udacity Machine Learning Engineer Nanodegree in June 2018.
convolutional-neural-networks udacity-machine-learning-nanodegree deep-learning speech-recognition mel-frequencies cepstral discrete-fourrier-transform discrete-cosine-transform python-3 tensorflow keras numpy scipy
Language:Jupyter Notebook 0
alain2208 / speech_feature_extraction
audio-analysis mfcc mel-frequencies
Language:Python