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https://github.com/nazir20/gender-classification-using-mfcc-with-lstm-model
Gender classification from audio signals is an important task with various applications, such as speech recognition systems, voice assistants, and speaker verification. This project aims to demonstrate how to use Mel Frequency Cepstral Coefficients as features and a Long Short-Term Memory (LSTM) neural network for gender classification.
https://github.com/nazir20/gender-classification-using-mfcc-with-lstm-model
audio-processing deep-learning deep-neural-networks gender-classification lstm lstm-neural-networks mfcc mfcc-extractor neural-networks
Last synced: about 1 month ago
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Gender classification from audio signals is an important task with various applications, such as speech recognition systems, voice assistants, and speaker verification. This project aims to demonstrate how to use Mel Frequency Cepstral Coefficients as features and a Long Short-Term Memory (LSTM) neural network for gender classification.
- Host: GitHub
- URL: https://github.com/nazir20/gender-classification-using-mfcc-with-lstm-model
- Owner: nazir20
- Created: 2023-07-24T13:06:45.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-07-24T16:02:03.000Z (over 1 year ago)
- Last Synced: 2023-07-24T18:25:26.604Z (over 1 year ago)
- Topics: audio-processing, deep-learning, deep-neural-networks, gender-classification, lstm, lstm-neural-networks, mfcc, mfcc-extractor, neural-networks
- Language: Jupyter Notebook
- Homepage:
- Size: 1.48 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md