https://github.com/anuragmishracse/audio_emotion_analysis
The library is useful for analyzing the emotions present in any audio file(call/music/recordings) into three classes namely positive, negative, neutral.
https://github.com/anuragmishracse/audio_emotion_analysis
audio audio-emotion-analysis emotion emotion-analysis extract-features feature-extraction
Last synced: about 2 months ago
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The library is useful for analyzing the emotions present in any audio file(call/music/recordings) into three classes namely positive, negative, neutral.
- Host: GitHub
- URL: https://github.com/anuragmishracse/audio_emotion_analysis
- Owner: anuragmishracse
- Created: 2016-07-02T07:44:59.000Z (over 9 years ago)
- Default Branch: master
- Last Pushed: 2016-07-26T12:32:02.000Z (about 9 years ago)
- Last Synced: 2025-04-18T16:10:37.972Z (6 months ago)
- Topics: audio, audio-emotion-analysis, emotion, emotion-analysis, extract-features, feature-extraction
- Language: Python
- Homepage:
- Size: 4.51 MB
- Stars: 31
- Watchers: 5
- Forks: 7
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# audio_emotion_analysis
The objective of this project is to predict the emotion present in any audio file/signal.
> Clone the project and move to the directory in shell/command line.
##Requirements:
1. Anaconda - This installs python along with most popular python libraries including sklearn. If not already installed, install it from https://www.continuum.io/downloads .
2. python_speech_features - Python library for feature extraction.
3. pyaudio - Python library for recording and playing of audio samples.
The requirements 2 and 3 can be installed by executing the following using shell/cmd, in the cloned repository directory:
```
pip install -r requirements.txt
```
##Installation:
Once the requirements are installed, just type the following in shell/cmd
`python setup.py install`
##Preparing dataset:
- Put all the unlabelled audio files in a folder named `calls`, or any other folder and update the name of folder in `label_dataset.py`.
- Run the script `label_dataset.py`.
- It will scan all the audio files, create a set of 30 sec audio chunks.
- It will play each chunk and then ask for a label (positive/neutral/negative).
- Enter `1` for `negative`, `2` for `neutral` and `3` for `positive`.
- Continue until all the chunks are labelled.
- A new dataset will be prepared in a new folder named `data`, with each audio file of the name in the format `_.wav`. For example:
```
> positive_1.wav
> negative_2.wav
```
## Scripts description:
### feature_extractor.py
Extracts the features from any audio file or signal and returns a feature vector.
### emotion_analysis.py
This is the backbone of the project. It contains modules that
- extracts features using the `feature_extractor.py`
- trains the model
- tests the model
- evaluates the model on new dataset
##Evaluating a new dataset:
- Put all the new audio files in a folder named `test_calls`, or any other folder and update the name of the folder in `main_script.py`.
- Execute the script `main_script.py` by typing the following in shell/cmd, in the containing directory:
```
python main_script.py
```
- The script will load an existing model or train a new model on the dataset prepared, extract features for each of the new audio files and feed it to the trained model.
###Results
- For each file, following three things are evaluated:
- Overall emotion
- Emotion transition from first half to second half
- Emotion present in each 20 sec chunk of the file
- These results for each audio file are written to a `.csv` file, on which further analysis can be done.