Project/scholarship details
- Project/scholarship ID
102185
- Reference
PTDC/EIA-EIA/102185/2008
- FundRef URI
- Funder
FCT - Fundação para a Ciência e a Tecnologia, I.P.
- Funder's country
Portugal
- Funding program
5876-PPCDTI
- Funding amount
77,304.00 €
- Start date
2010-05-16
- End date
2013-11-15
Documents
Automatic Creation of Mood Playlists in the Thayer Plane: A Methodology and a C...
Panda, Renato; Paiva, Rui PedroWe propose an approach for the automatic creation of mood playlists in the Thayer plane (TP). Music emotion recognition is tackled as a regression and classification problem, aiming to predict the arousal and valence (AV) values of each song in the TP, based on Yang's dataset. To this end, a high number of audio features are extracted using three frameworks: PsySound, MIR Toolbox and Marsyas. The extracted feat...
MOODetector: A Prototype Software Tool for Mood-based Playlist Generation
Cardoso, Luís; Panda, Renato; Paiva, Rui PedroWe propose a prototype software tool for the automatic generation of mood-based playlists. The tool works as typical music player, extended with mechanisms for automatic estimation of arousal and valence values in the Thayer plane (TP). Playlists are generated based on one seed song or a desired mood trajectory path drawn by the user, according to the distance to the seed(s) in the TP. Besides playlist generati...
Using Support Vector Machines for Automatic Mood Tracking in Audio Music
Panda, Renato; Paiva, Rui PedroIn this paper we propose a solution for automatic mood tracking in audio music, based on supervised learning and classification. To this end, various music clips with a duration of 25 seconds, previously annotated with arousal and valence (AV) values, were used to train several models. These models were used to predict quadrants of the Thayer’s taxonomy and AV values, of small segments from full songs, revealin...
MIREX 2012: Mood Classification Tasks Submission
Panda, Renato; Paiva, Rui PedroShort paper describing our MIREX 2012 Audio Mood Classification Task submission (1st place).; In this work, three audio frameworks – Marsyas, MIR Toolbox and PsySound3, were used to extract audio features from the audio samples. These features are then used to train several classification models, resulting in the different versions submitted to MIREX 2012 mood classification task.; This work was supported by th...
Music Emotion Classification: Analysis of a Classifier Ensemble Approach
Panda, Renato; Paiva, Rui PedroWe propose a five regression models’ system to classify music emotion. To this end, a dataset similar to MIREX contest dataset was used. Songs from each cluster are separated in five sets and labeled as 1. A similar number of songs from other clusters are then added to each set and labeled 0, training regression models to output a value representing how much a song is related to the specific cluster. The five o...
Music Emotion Classification: Dataset Acquisition and Comparative Analysis
Panda, Renato; Paiva, Rui PedroIn this paper we present an approach to emotion classification in audio music. The process is conducted with a dataset of 903 clips and mood labels, collected from Allmusic database, organized in five clusters similar to the dataset used in the MIREX Mood Classification Task. Three different audio frameworks - Marsyas, MIR Toolbox and Psysound, were used to extract several features. These audio features and ann...
Multi-Modal Music Emotion Recognition: A New Dataset, Methodology and Comparati...
Panda, Renato Eduardo Silva; Malheiro, Ricardo; Rocha, Bruno; Oliveira, António Pedro; Paiva, Rui PedroWe propose a multi-modal approach to the music emotion recognition (MER) problem, combining information from distinct sources, namely audio, MIDI and lyrics. We introduce a methodology for the automatic creation of a multi-modal music emotion dataset resorting to the AllMusic database, based on the emotion tags used in the MIREX Mood Classification Task. Then, MIDI files and lyrics corresponding to a sub-set of...
Dimensional Music Emotion Recognition: Combining Standard and Melodic Audio Fea...
Panda, Renato; Rocha, Bruno; Paiva, Rui PedroWe propose an approach to the dimensional music emotion recognition (MER) problem, combining both standard and melodic audio features. The dataset proposed by Yang is used, which consists of 189 audio clips. From the audio data, 458 standard features and 98 melodic features were extracted. We experimented with several supervised learning and feature selection strategies to evaluate the proposed approach. Employ...
Music Emotion Recognition from Lyrics: A Comparative Study
Malheiro, Ricardo; Panda, Renato; Gomes, Paulo J. S.; Paiva, Rui PedroWe present a study on music emotion recognition from lyrics. We start from a dataset of 764 samples (audio+lyrics) and perform feature extraction using several natural language processing techniques. Our goal is to build classifiers for the different datasets, comparing different algorithms and using feature selection. The best results (44.2% F-measure) were attained with SVMs. We also perform a bi-modal analys...
Music Emotion Recognition: The Importance of Melodic Features
Rocha, Bruno; Panda, Renato; Paiva, Rui PedroWe 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 improv...
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