M-VGGish embedding
This repository contains example codes to extract audio features using M-VGGish model presented in the following papers
- Bongjun Kim and Bryan Pardo, “Improving Content-based Audio Retrieval by Vocal Imitation Feedback,” IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Brighton, UK, 2019. [pdf]
@inproceedings{kim2019improving,
title={Improving content-based audio retrieval by vocal imitation feedback},
author={Kim, Bongjun and Pardo, Bryan},
booktitle={ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={4100--4104},
year={2019},
organization={IEEE}
}
M-VGGish also has been used in the following paper
- Fatemeh Pishdadian, Bongjun Kim, Prem Seetharaman, and Bryan Pardo, “Classifying non-speech vocals: Deep vs Signal Processing Representations,” Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE) 2019. [pdf]
Code description
-
Some of the feature extraction codes (
vggish_utils/
) are from the repository of VGGish model. (vggish_input_clipwise.py
was newly added to extract a clip-level feature vector.) -
It takes a mel-spectrogram of a recording (any length) and outputs a 8192-dimensional feature vector.
-
You will need
python 3
withPyTorch
andlibrosa
-
Run the example code (It runs on CPU).
python run.py