Machine learning for children’s music emotion recognition
Μηχανική μάθηση για την αναγνώριση συναισθήματος σε παιδική μουσική
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Keywords
Machine learning ; Music emotion recognition ; Deep learning ; Convolutional neural network ; LSTM ; Attention ; Music information retrievalAbstract
This work focuses on the application of Machine Learning techniques for Music Emotion Recognition, particularly focusing on children's music. The first step was to create a specialized dataset for children's music, which includes songs of varied emotions and cultural backgrounds, annotated by experts in child psychology and education, as well as Machine Learning Engineers. A Support Vector Machine was employed as a baseline model for the prediction task, to process a range of handcrafted audio features. Concerning more advanced models, Convolutional Neural Networks and a Dual-Stream architecture model, integrating both Convolutional and attention-based Long Short-Term Memory networks were evaluated. This approach offers a comprehensive analysis of children's music by examining both spectrograms and music transcription sequences. Models were evaluated using the Probabilistic Emotion Alignment to compare model posteriors with the probability distribution of expert annotations. Moreover, models evaluated using the established Machine Learning metrics, indicating that different modalities are able to enhance the predictive capacity for emotion recognition.