Clean Audio for TV broadcast: An Object-Based Approach for Hearing-Impaired Viewers - April 2015
Audibility of a CD-Standard A/DA/A Loop Inserted into High-Resolution Audio Playback - September 2007
Sound Board: Food for Thought, Aesthetics in Orchestra Recording - April 2015
Evaluation of Acoustic Features for Music Emotion Recognition
Classification of music by mood is a growing area of research with interesting applications, including navigation of large music collections. Mood classifiers are usually based on acoustic features extracted from the music, but often they are used without knowing which ones are most effective. This paper describes how 63 acoustic features were evaluated using 2,389 music tracks to determine their individual usefulness in mood classification, before using feature selection algorithms to find the optimum combination.
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.