https://github.com/fazledyn/gender-classification-from-audio-clips

In this project, we built a machine learning model that can identify the gender of a person from their voice recording.
https://github.com/fazledyn/gender-classification-from-audio-clips

deep-learning gender-classification machine-learning tensorflow

Last synced: about 2 months ago
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In this project, we built a machine learning model that can identify the gender of a person from their voice recording.

• Host: GitHub
• URL: https://github.com/fazledyn/gender-classification-from-audio-clips
• Owner: fazledyn
• Created: 2023-02-20T16:29:24.000Z (almost 2 years ago)
• Default Branch: master
• Last Pushed: 2023-03-31T10:19:21.000Z (almost 2 years ago)
• Last Synced: 2024-03-17T14:01:11.668Z (10 months ago)
• Topics: deep-learning, gender-classification, machine-learning, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 654 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
![Project Cover Image](/docs/cover.svg)

# Overview

In this project, we aim to build a machine learning model that can identify the gender of a person from their voice recording. In the process, we use two intermediary data representation format of the audio clips- **Mel Spectrogram** (Mel) and **Mel-Frequency Cepstral Coefficients** (MFCC).

# Datasets

**[MCV]** Common Voice by Mozilla.org (https://www.kaggle.com/datasets/mozillaorg/common-voice)

**[DLS]** Bengali Common Voice Speech Dataset (https://www.kaggle.com/competitions/dlsprint)

# Proposed Solution

## Mel-Frequency Cepstral Coefficients (MFCC)

![Project Cover Image](/docs/mfcc.png)

## Mel Spectrogram

![Project Cover Image](/docs/mel.png)

# Notebook Details

## Training

The `training` folder contains four notebooks. Each of the notebooks are named as: `[Data-Type]_[Dataset]_[Model]`. These notebooks are used to train individual models on the train datasets.

```      

└── training

    ├── mel_dls_resnet50_train.ipynb 

    ├── mel_mcv_resnet50.ipynb       

    ├── mfcc_dls_train_resnet50.ipynb

    └── mfcc_mcv_resnet50.ipynb

```

## Evaluation

The `evaluation` folder contains four notebooks. Each of the notebooks are named as: `[Data-Type]_[Datase#1]_on_[Dataset#2]`. The models trained on `Dataset#1` are used to evaluate `Dataset#2`.

```

└── evaluation

    ├── mel_dls_on_mcv.ipynb

    ├── mel_mcv_on_dls.ipynb

    ├── mfcc_dls_on_mcv.ipynb        

    └── mfcc_mcv_on_dls.ipynb

```

In the report mentioned in the [presentation](#presentation-report), the comparison between models are shown.

# Model Details

- Architecture: ResNet50

- Learning Rate: 0.0001

- Adam Optimizer

# Presentation Report

https://docs.google.com/presentation/d/14BWOq6YSmO3GqZHEvCou43Z5A4dlOmKq4pjqUqgZALU/

# References

[1]  **Speaker Gender Recognition Based on Deep Neural Networks and ResNet50** (https://doi.org/10.1155/2022/4444388)

[2]  **A Machine Learning Approach to Automating Bengali Voice Based Gender Classification** (https://ieeexplore.ieee.org/document/9117385)