3 Application of Machine Learning in Music Analytics

Velankar, Makarand; Deshpande, Amod; Kulkarni, Parag

doi:10.1515/9783110610987-005

Cited by 3 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Music emotions or sentiment analysis has been a topic of research and different approaches have been proposed such as use of lyrics [29], multimodal approach [30] and use of machine learning [31]. Use of soft computing approach with neural network [32] is further extended with deep neural networks.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Deep Learning Models for Music Emotion Recognition

Velankar¹,

Thombre²,

Wadkar³

2022

IJEAST

View full text Add to dashboard Cite

Music listening helps people not only for entertainment, but also to reduce emotional stress in their daily lives. People nowadays tend to use online music streaming services such as Spotify, Amazon Music, Google Play Music, etc. rather than storing the songs on their devices. The songs in these streaming services are categorized into different emotional labels such as happy, sad, romantic, devotional, etc. In the music streaming applications, the songs are manually tagged with their emotional categories for music recommendation. Considering the growth of music on different social media platforms and the internet, the need for automatic tagging will increase in coming time. The work presented deals with training the deep learning model for automatic emotional tagging. It covers implementation of two different deep learning architectures for classifying the audio files using the Mel-spectrogram of music audio. The first architecture proposed is Convolutional Recurrent Model (CRNN) and the second architecture is a Parallel Convolutional Recurrent Model (Parallel CNN). Both the architectures exploit the combined features of Convolutional and Recurrent layers. This combination is used to extract features from time and frequency domains. The results with accuracies in the range of 51 to 54 % are promising for both models for a small dataset of 138 songs, considering the large datasets required for training deep learning models.

show abstract

Section: Introductionmentioning

confidence: 99%