https://github.com/hrichharms/ffmpeg_filters_mse

Calculates and visualizes the temporal domain and frequency domain mean squared error of ffmpeg audio filters
https://github.com/hrichharms/ffmpeg_filters_mse

audio-filters audio-processing ffmpeg mean-square-error psnr

Last synced: 2 months ago
JSON representation

Calculates and visualizes the temporal domain and frequency domain mean squared error of ffmpeg audio filters

• Host: GitHub
• URL: https://github.com/hrichharms/ffmpeg_filters_mse
• Owner: hrichharms
• Created: 2022-03-15T02:09:03.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2022-03-18T21:08:57.000Z (about 3 years ago)
• Last Synced: 2025-02-14T17:43:21.872Z (4 months ago)
• Topics: audio-filters, audio-processing, ffmpeg, mean-square-error, psnr
• Language: Python
• Homepage:
• Size: 58.6 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# ffmpeg_filters_mse

Calculates and visualizes the temporal domain and frequency domain mean squared error of [ffmpeg audio filters](https://ffmpeg.org/ffmpeg-filters.html).

## Setup

1. Install [ffmpeg](https://ffmpeg.org/download.html)

2. Install required Python packages ( `pip3 install -r requirements.txt` )

3. Create `original_audio` directory

4. Put audio in `original_audio` directory

## Usage

1. Run `convert.sh` if audio files are not in .wav format

2. Run `apply_filters.py` to create filtered audio files

3. Run `mse.py` to calculate the mean square error for each filter

4. Run `visualize.py` to visualize the results as bar graphs

## Repository Files

### convert.sh

Converts any audio files in `original_audio` into a .wav file using default ffmpeg conversion and deletes originals.

#### Usage

`./convert.sh`

### filters.json

JSON formatted list of ffmpeg audio filters

### config.json

Configuration file for `apply_filters.py` and `mse.py`

| key                 | default         | description                                                                   |

|---------------------|-----------------|-------------------------------------------------------------------------------|

| filters_filename    | filters.json    | filename of JSON formatted filters list in                                    |

| segment_len         | 262144          | number of audio samples in each analyzed segment                              |

| sample_skips        | 262144          | number of samples skipped between beginnings of analyzed segments             |

| bit_depth           | 16              | bit depth of analyzed audio                                                   |

| original_audio_dir  | original_audio  | relative path to search for original audio                                    |

| filtered_audio_dir  | filtered_audio  | relative path of filtered audio                                               |

| output_filename     | output.json     | filename of JSON formatted mean square error output                           |

### config.py

Defines `CONFIG_FILENAME`, `Config` class, and associated JSON loader function (`load_config`).

### apply_filters.py

Loads configuration from `CONFIG_FILENAME`, applies list of ffmpeg audio filters from `filters_filename` to .wav files in `original_audio_dir` and writes resulting audio files to `filtered_audio_dir`.

#### Usage

`python3 apply_filters.py`

### mse.py

Loads configuration from `CONFIG_FILENAME` and calculates the average MSE of sequences of length `sequence_len` in the temporal domain and frequency domain ([DCT-II](https://en.wikipedia.org/wiki/Discrete_cosine_transform#DCT-II)) between original audio segments and their filtered counterparts. Resulting MSEs are dumped to `output_filename` in JSON format.

#### Usage

`python3 mse.py`

### visualize.py

Loads configuration from `CONFIG_FILENAME`, read MSE outputs from `output_filename` and plot the results as bar graphs.

## Example Results

The following results were calculated from 3 hours of audio extracted from a Twitch VOD.

| filter      | MSE (temporal domain) | MSE (frequency domain) |

|-------------|-----------------------|------------------------|

| acompressor | 419.5447047722049     | 769325.3616135248      |

| acrusher    | 128.31195087665463    | 788.4744883700115      |

| aecho       | 1973.808181613829     | 11476890.952585308     |

| aphaser     | 2140.157159476164     | 7514830.79328153       |

| alimiter    | 1589.4807644937096    | 33103402.4035865       |

### Temporal Domain MSE



### Frequency Domain MSE



## TODO

- Add more audio filters

- Add better documentation for example results