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x4nth055 / emotion-recognition-using-speech

Star 539

Building and training Speech Emotion Recognizer that predicts human emotions using Python, Sci-kit learn and Keras

machine-learning deep-learning sklearn keras recurrent-neural-networks feature-extraction neural-networks support-vector-machine mfcc librosa emotion-detection gradient-boosting emotion-recognition kneighborsclassifier random-forest-classifier mlp-classifier speech-emotion-recognition emotion-recognizer
  • Updated Nov 3, 2023
  • Python

gionanide / Speech_Signal_Processing_and_Classification

Star 232

Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can…

nlp classifier natural-language-processing feature-extraction nltk gaussian-mixture-models support-vector-machines mfcc principal-component-analysis speech-processing linear-discriminant-analysis isomap spectral-clustering long-short-term-memory kernel-pca spectral-embedding locally-linear-embedding linear-prediction-coefficients speech-utterance
  • Updated Mar 3, 2023
  • Python

georgid / AlignmentDuration

Star 56

Lyrics-to-audio-alignement system. Based on Machine Learning Algorithms: Hidden Markov Models with Viterbi forced alignment. The alignment is explicitly aware of durations of musical notes. The phonetic model are classified with MLP Deep Neural Network.

python music duration synchronization research deep-learning signal-processing lyrics decoding music-information-retrieval neural-networks alignment hidden-markov-model gmm mfcc upf htk
  • Updated Mar 9, 2020
  • Python

linksense / ConvolutionaNeuralNetworksToEnhanceCodedSpeech

Star 28

In this work we propose two postprocessing approaches applying convolutional neural networks (CNNs) either in the time domain or the cepstral domain to enhance the coded speech without any modification of the codecs. The time domain approach follows an end-to-end fashion, while the cepstral domain approach uses analysis-synthesis with cepstral d…

keras cnn generative-adversarial-network gan post-processing mfcc speech-processing wasserstein-gan speech-enhancement speech-reconstruction 1d-convolution
  • Updated Mar 8, 2020
  • Python

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