The main idea is to take data from the PMEmo dataset for Music Emotion Computing and find better results in field of Music Emotion Recognition (MER).
PMEmo is a popular music dataset with emotional annotations:
It contain emotion annotations of 794 songs as well as the simultaneous electrodermal activity (EDA) signals. A Music Emotion Experiment was well-designed for collecting the affective-annotated music corpus of high quality, which recruited 457 subjects.
Article: https://dl.acm.org/citation.cfm?id=3206037
To briefly summarize, the PMEmo dataset provides:
- song metadata (song title, artists, beginning and ending timestamps of chorus section);
- manually selected music chorus clips (.mp3);
- pre-computed audio features for use in MER tasks;
- manually annotated emotion labels: static labels for the whole clips (i.e., overall labels), and dynamic labels for each 0.5-second segment (i.e., continuous labels over time);
- Corresponding EDA physiological signals;
- song lyrics (.lrc);
- song comments from online music websites (Chinese and English texts).
Whole dataset available at https://drive.google.com/drive/folders/1qDk6hZDGVlVXgckjLq9LvXLZ9EgK9gw0?usp=sharing the acoustic features and the annotations are all stored in CSV files (delimited by comma).
The data about the features extraction etc. can be downloaded at https://mega.nz/#F!b4MAzIBI!X9lQqEUzETixcimU0d6RCw
In the code folder there are all the jupyter notebooks:
PMEmo_implementation: code to get the same results of the PMEmo articlefeature_extraction: generation of .csv file with all songs features and EDA files.audio:features: explanation and visualization of all the audio features extractedfeatures_extractor: funcion that takes audio folder and return the .csv file with audio features
eda:eda_features: explanation and visualization of all the eda features extractededa_features_extractor: funcion that takes eda folder and return the .csv file with eda features
feature_selection: implementation of different algorithms of features selection based on the state of the art:- Pearson correlation
- Backward elimination
- RFE (Recursive Feature Elimination)
- embedded method
- RreliefF algorithm
MER: Music Emotion Recognition, where is evaluated performances in RMSE sense of various regressors based on audio, eda, combination of audio and eda and with no feature selection or different set of selected features.
Trying to reproduce article of [EDAGram] (https://ieeexplore.ieee.org/document/7590725), that implement a spectrogram for electrodermal activities, in eda_gram folder. This combinated with spectrogram folder will allow to use CNN neural networks based on EDA-Gram and spectrogram.