# audio_classification
**Repository Path**: rsmeng/audio_classification
## Basic Information
- **Project Name**: audio_classification
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 1
- **Forks**: 0
- **Created**: 2020-05-07
- **Last Updated**: 2020-12-19
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# Audio classification using CNN and LSTM
## Data Visualization
### MFCC Features
### Spectrogram
### Raw Audio
## Results
| CNN |
|
Spectrogram |
MFCC |
| Dataset |
Train |
Validation |
Train |
Validation |
| urbansound8k |
99.914 |
97.252 |
100 |
84.544 |
#
| CNN-LSTM |
|
Spectrogram |
MFCC |
| Dataset |
Train |
Validation |
Train |
Validation |
| urbansound8k |
99.928 |
96.451 |
99.985 |
82.369 |
### Training Accuracy Plot
### Validation Accuracy Plot
### Training Error Plot
### Validation Error Plot
## Installation
Use the package manager [pip](https://pip.pypa.io/en/stable/) to install foobar.
```bash
pip install requirements.txt
```
or
```bash
conda create --name --file requirements.txt
```
## Usage
### Dataset
[urbansound8k]
### Pre-process Data
```bash
python codes/pre_processing/pre_processing_urbansound.py
```
### Train and Test
```bash
python codes/baseline/main.py
```
[urbansound8k]: [https://urbansounddataset.weebly.com/urbansound8k.html]
## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.
## License
[MIT](https://choosealicense.com/licenses/mit/)