AES E-Library

AES E-Library

Prediction of Valence and Arousal from Music Features

Document Thumbnail

Mood is an important attribute of music, and knowledge on mood can be used as a basic ingredient in music recommender and retrieval systems. Moods are assumed to be dominantly determined by two dimensions: valence and arousal. An experiment was conducted to attain data for song-based ratings of valence and arousal. It is shown that subject-averaged valence and arousal can be predicted from music features by a linear model.

AES Convention: eBrief:
Publication Date:

Click to purchase paper as a non-member or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member and would like to subscribe to the E-Library then Join the AES!

This paper costs $33 for non-members and is free for AES members and E-Library subscribers.

Learn more about the AES E-Library

The Engineering Briefs at this Convention were selected on the basis of a submitted synopsis, ensuring that they are of interest to AES members, and are not overly commercial. These briefs have been reproduced from the authors' advance manuscripts, without editing, corrections, or consideration by the Review Board. The AES takes no responsibility for their contents. Paper copies are not available, but any member can freely access these briefs. Members are encouraged to provide comments that enhance their usefulness.

Start a discussion about this paper!

AES - Audio Engineering Society