AMBIENTUM BIOETHICA BIOLOGIA CHEMIA DIGITALIA DRAMATICA EDUCATIO ARTIS GYMNAST. ENGINEERING EPHEMERIDES EUROPAEA GEOGRAPHIA GEOLOGIA HISTORIA HISTORIA ARTIUM INFORMATICA IURISPRUDENTIA MATHEMATICA MUSICA NEGOTIA OECONOMICA PHILOLOGIA PHILOSOPHIA PHYSICA POLITICA PSYCHOLOGIA-PAEDAGOGIA SOCIOLOGIA THEOLOGIA CATHOLICA THEOLOGIA CATHOLICA LATIN THEOLOGIA GR.-CATH. VARAD THEOLOGIA ORTHODOXA THEOLOGIA REF. TRANSYLVAN
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Authors: ANDREI PETRESCU.
DOI: 10.24193/subbi.2022.1.04
Published Online: 2022-07-03
pp. 45-60
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This paper proposes a method for music recommendations using emotions, using deep learning techniques. The method is composed of two modules. The emotion detection module, which utilizes a hybrid architecture involving a Convolutional Neural Network (CNN) and a Reccurent Neural Network using Long-Short Term Memory (LSTM) Cells. We compared individual architectures of CNNs and LSTMs against our hybrid approach, outperforming them during experiments. We evaluated the modules on our own data set, created using Spotify’s API and containing 2028 songs from different genres and linguistic families, labeled with valence and arousal values. The model also outperforms other related approaches, however we did not evaluate them on the same data set. The predictions are used by the second module, for which we proposed a simple method of ordering the results based on the similarity to user’s input.
Received by the editors: 10 October 2021.
2010 Mathematics Subject Classification. 68T45.
1998 CR Categories and Descriptors. I.4.8 [Image Processing and Computer Vision]: Scene Analysis – Object recognition; I.2.6 [Artificial Intelligence]: Learning – Connectionism and neural nets; I.2.10 [Artificial Intelligence]: Vision and Scene Understanding – Intensity, color, photometry, and thresholding.
Key words and phrases. mood, emotion, valence, energy, convolutional neural network, recurrent neural networks, long-short term memory, hybrid, regression, classification.
