https://github.com/singhvishal003/audio-classification
Audio classification using Tensorflow.
https://github.com/singhvishal003/audio-classification
matplotlib tensorflow tensorflow-gpu tensorflow-io
Last synced: 9 months ago
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Audio classification using Tensorflow.
- Host: GitHub
- URL: https://github.com/singhvishal003/audio-classification
- Owner: Singhvishal003
- Created: 2024-11-22T13:31:04.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-11-23T07:25:28.000Z (about 1 year ago)
- Last Synced: 2025-01-28T04:31:19.148Z (11 months ago)
- Topics: matplotlib, tensorflow, tensorflow-gpu, tensorflow-io
- Language: Jupyter Notebook
- Homepage:
- Size: 5.86 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
## Audio Classification Model
### Overview
This project involves building an audio classification model using machine learning techniques. The model is designed to classify audio signals into predefined categories based on their features.
### Dataset
The dataset used for training and testing the model consists of labeled audio files. Each audio file is associated with a specific class label, representing the category it belongs to.
### Preprocessing
1. *Audio Loading*: Audio files are loaded and converted into a consistent format.
2. *Feature Extraction*: Key features such as Mel-frequency cepstral coefficients (MFCCs), chroma features, and spectral contrast are extracted from the audio signals.
3. *Normalization*: The extracted features are normalized to ensure uniformity across the dataset.
### Model Architecture
The model is built using a deep learning framework, leveraging a convolutional neural network (CNN) for feature learning and classification. The architecture includes:
- *Input Layer*: Accepts the preprocessed audio features.
- *Convolutional Layers*: Extracts spatial hierarchies of features.
- *Pooling Layers*: Reduces the dimensionality of the feature maps.
- *Fully Connected Layers*: Performs the final classification based on the learned features.
- *Output Layer*: Produces the probability distribution over the classes.
### Training
The model is trained using a supervised learning approach. The dataset is split into training and validation sets. The training process involves:
- *Loss Function*: Categorical cross-entropy is used as the loss function.
- *Optimizer*: Adam optimizer is employed to minimize the loss.
- *Evaluation Metrics*: Accuracy, precision, recall, and F1-score are used to evaluate the model's performance.
### Results
The trained model achieves high accuracy on the validation set, demonstrating its effectiveness in classifying audio signals. Detailed performance metrics and confusion matrix are provided in the results section.
### Usage
To use the model for audio classification:
1. *Load the Model*: Load the pre-trained model from the provided file.
2. *Preprocess Audio*: Preprocess the input audio file to extract features.
3. *Predict*: Use the model to predict the class of the audio signal.
### Conclusion
This audio classification model provides a robust solution for categorizing audio signals. Future work includes exploring more advanced architectures and larger datasets to further improve performance.