Music Emotion Recognition Based on Chord Identification

As one of the most classic human inventions, music can be seen as another language, used to express the author’s thoughts and emotion. Music Emotion Recognition (MER) is an interesting research topic in artificial intelligence field for recognising emotions from music. The recognition methods and tools for music signals are growing fast recently. However the attentions are paid mainly on music signal feature extraction and machine learning methods. The theoretical knowledge of music on emotion is usually ignored. This paper propose a new method through important music units – chord to recognize emotion from music. We firstly build a new chord database with chord segments based on selected emotions. Then FFT and statistics (STAT) features are extracted from the music signals in the database. Then one-shot learning are used for chord identification and emotion recognition using Euclidean distance and correlation. The proposed method can recognise 6 emotions from music signals through chord identification with 76.6% accuracy of chord identification. This idea can be developed further using more advanced feature extraction and one-shot learning methods.