<p>We study the importance of a melodic audio (MA) feature set in music emotion recognition (MER) and compare its performance to an approach using only standard audio (SA) features. We also analyse the fusion of both types of features. Employing only SA features, the best attained performance was 46.3%, while using only MA features the best outcome was 59.1% (F- measure). A combination of SA and MA features improved results to 64%. These results might have an important impact to help break the so-called glass ceiling in MER, as most current approaches are based on SA features.</p
Part 9: Music Information Processing WorkshopInternational audienceIn this paper, we decided to stud...
Empirical thesis.Bibliography: pages 45-49.1. Introduction -- 2. Music features -- 3. Emotion models...
Features are arguably the key factor to any machine learning problem. Over the decades, myriads of a...
We study the importance of a melodic audio (MA) feature set in music emotion recognition (MER) and c...
<p>We propose an approach to the dimensional music emotion recognition (MER) problem, combining both...
Abstract. We propose an approach to the dimensional music emotion recognition (MER) problem, combini...
We propose a novel approach to music emotion recognition by combining standard and melodic features ...
The design of meaningful audio features is a key need to advance the state-of-the-art in Music Emoti...
We present a set of novel emotionally-relevant audio features to help improving the classification o...
The aim of this paper was to discover what combination of audio features gives the best performance ...
<p>We propose a multi-modal approach to the music emotion recognition (MER) problem, combining infor...
The high feature dimensionality is a challenge in music emotion recognition. There is no common cons...
Music emotion recognition (MER) as a part of music information retrieval (MIR), examines the questio...
Abstract. We propose a multi-modal approach to the music emotion recognition (MER) problem, combinin...
Detecting emotion features in a song remains as a challenge in various area of research especially i...
Part 9: Music Information Processing WorkshopInternational audienceIn this paper, we decided to stud...
Empirical thesis.Bibliography: pages 45-49.1. Introduction -- 2. Music features -- 3. Emotion models...
Features are arguably the key factor to any machine learning problem. Over the decades, myriads of a...
We study the importance of a melodic audio (MA) feature set in music emotion recognition (MER) and c...
<p>We propose an approach to the dimensional music emotion recognition (MER) problem, combining both...
Abstract. We propose an approach to the dimensional music emotion recognition (MER) problem, combini...
We propose a novel approach to music emotion recognition by combining standard and melodic features ...
The design of meaningful audio features is a key need to advance the state-of-the-art in Music Emoti...
We present a set of novel emotionally-relevant audio features to help improving the classification o...
The aim of this paper was to discover what combination of audio features gives the best performance ...
<p>We propose a multi-modal approach to the music emotion recognition (MER) problem, combining infor...
The high feature dimensionality is a challenge in music emotion recognition. There is no common cons...
Music emotion recognition (MER) as a part of music information retrieval (MIR), examines the questio...
Abstract. We propose a multi-modal approach to the music emotion recognition (MER) problem, combinin...
Detecting emotion features in a song remains as a challenge in various area of research especially i...
Part 9: Music Information Processing WorkshopInternational audienceIn this paper, we decided to stud...
Empirical thesis.Bibliography: pages 45-49.1. Introduction -- 2. Music features -- 3. Emotion models...
Features are arguably the key factor to any machine learning problem. Over the decades, myriads of a...