AES Store

Journal Forum

Audibility of a CD-Standard A/DA/A Loop Inserted into High-Resolution Audio Playback - September 2007

Reflecting on Reflections - June 2014

Quiet Thoughts on a Deafening Problem - May 2014
1 comment

Access Journal Forum

AES E-Library

Selection of Audio Features for Music Emotion Recognition Using Production Music

Music emotion recognition typically attempts to map audio features from music to a mood representation using machine learning techniques. In addition to having a good dataset, the key to a successful system is choosing the right inputs and outputs. Often, the inputs are based on a set of audio features extracted from a single software library, which may not be the most suitable combination. This paper describes how 47 different types of audio features were evaluated using a five-dimensional support vector regressor, trained and tested on production music, in order to find the combination which produces the best performance. The results show the minimum number of features that yield optimum performance, and which combinations are strongest for mood prediction.

AES Conference:
Paper Number:
Publication Date:

Click to purchase paper 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 $20 for non-members, $5 for AES members and is free for E-Library subscribers.

Learn more about the AES E-Library

E-Library Location:

Start a discussion about this paper!

Facebook   Twitter   LinkedIn   Google+   YouTube   RSS News Feeds  
AES - Audio Engineering Society