A New Multilabel System for Automatic Music Emotion Recognition

Achieving advancements in automatic recognition of emotions that music can induce require considering multiplicity and simultaneity of emotions. Comparison of different machine learning algorithms performing multilabel and multiclass classification is the core of our work. The study analyzes the implementation of the Geneva Emotional Music Scale 9 in the Emotify music dataset and investigate its adoption from a machine-learning perspective. We approach the scenario of emotions expression/induction through music as a multilabel and multiclass problem, where multiple emotion labels can be adopted for the same music track by each annotator (multilabel), and each emotion can be identified or not in the music (multiclass). The aim is the automatic recognition of induced emotions through music.

[1]  Remco C. Veltkamp,et al.  Collecting annotations for induced musical emotion via online game with a purpose emotify , 2014 .

[2]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[3]  Elia Bruni,et al.  Multimodal Distributional Semantics , 2014, J. Artif. Intell. Res..

[4]  Björn W. Schuller,et al.  DEMoS: an Italian emotional speech corpus , 2019, Language Resources and Evaluation.

[5]  Petri Toiviainen,et al.  Exploring relationships between audio features and emotion in music , 2009 .

[6]  Gert R. G. Lanckriet,et al.  Semantic Annotation and Retrieval of Music and Sound Effects , 2008, IEEE Transactions on Audio, Speech, and Language Processing.

[7]  Remco C. Veltkamp,et al.  Computational Modeling of Induced Emotion Using GEMS , 2014, ISMIR.

[8]  Giovanni Saggio,et al.  A data glove based sensor interface to expressively control musical processes , 2011, 2011 4th IEEE International Workshop on Advances in Sensors and Interfaces (IWASI).

[9]  David Huron,et al.  Exploring the Musical Mind: Cognition, Emotion, Ability, Function , 2005 .

[10]  Jeffrey J. Scott,et al.  MUSIC EMOTION RECOGNITION: A STATE OF THE ART REVIEW , 2010 .

[11]  György Fazekas,et al.  Music Emotion Recognition: From Content- to Context-Based Models , 2012, CMMR.

[12]  Roberto Basili,et al.  SVM based transcription system with short-term memory oriented to polyphonic piano music , 2010, Melecon 2010 - 2010 15th IEEE Mediterranean Electrotechnical Conference.

[13]  Björn W. Schuller,et al.  Identifying Emotions in Opera Singing: Implications of Adverse Acoustic Conditions , 2018, ISMIR.

[14]  Mark A. Hall,et al.  Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning , 1999, ICML.

[15]  Pat Langley,et al.  Estimating Continuous Distributions in Bayesian Classifiers , 1995, UAI.

[16]  Colin G. Johnson,et al.  Creative autonomy in a simple interactive music system , 2020, Journal of New Music Research.

[17]  Giovanni Saggio,et al.  Machine-Learning Analysis of Voice Samples Recorded through Smartphones: The Combined Effect of Ageing and Gender , 2020, Sensors.

[18]  John C. Platt,et al.  Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .

[19]  Igor Kononenko,et al.  On Biases in Estimating Multi-Valued Attributes , 1995, IJCAI.

[20]  Yi-Hsuan Yang,et al.  A Regression Approach to Music Emotion Recognition , 2008, IEEE Transactions on Audio, Speech, and Language Processing.

[21]  Björn W. Schuller,et al.  Categorical vs Dimensional Perception of Italian Emotional Speech , 2018, INTERSPEECH.

[22]  Rainer Reisenzein,et al.  Experiencing activation: energetic arousal and tense arousal are not mixtures of valence and activation. , 2002, Emotion.

[23]  Björn W. Schuller,et al.  Speech emotion recognition combining acoustic features and linguistic information in a hybrid support vector machine-belief network architecture , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[24]  Gert R. G. Lanckriet,et al.  Combining audio content and social context for semantic music discovery , 2009, SIGIR.

[25]  K. Scherer,et al.  Emotions evoked by the sound of music: characterization, classification, and measurement. , 2008, Emotion.

[26]  Petri Toiviainen,et al.  A Matlab Toolbox for Music Information Retrieval , 2007, GfKl.

[27]  Ronen Feldman,et al.  The Data Mining and Knowledge Discovery Handbook , 2005 .

[28]  Yi Lin,et al.  Exploration of Music Emotion Recognition Based on MIDI , 2013, ISMIR.

[29]  C. V. D. Malsburg,et al.  Frank Rosenblatt: Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms , 1986 .

[30]  L. Cronbach.  Coefficient alpha and the internal structure of tests , 1951 .

[31]  Lior Rokach,et al.  Data Mining And Knowledge Discovery Handbook , 2005 .

[32]  Giovanni Costantini,et al.  Speech emotion recognition using amplitude modulation parameters and a combined feature selection procedure , 2014, Knowl. Based Syst..

[33]  Mert Bay,et al.  The 2007 MIREX Audio Mood Classification Task: Lessons Learned , 2008, ISMIR.