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Deep Neural Network approaches for Analysing Videos of Music Performances

2022-05-05 09:04:04
Foteini Simistira Liwicki, Richa Upadhya, Prakash Chandra Chhipa, Killian Murphy, Federico Visi, Stefan Östersjö, Marcus Liwicki
arXiv_AI
         
Abstract

This paper presents a framework to automate the labelling process for gestures in musical performance videos with a 3D Convolutional Neural Network (CNN). While this idea was proposed in a previous study, this paper introduces several novelties: (i) Presents a novel method to overcome the class imbalance challenge and make learning possible for co-existent gestures by batch balancing approach and spatial-temporal representations of gestures. (ii) Performs a detailed study on 7 and 18 categories of gestures generated during the performance (guitar play) of musical pieces that have been video-recorded. (iii) Investigates the possibility to use audio features. (iv) Extends the analysis to multiple videos. The novel methods significantly improve the performance of gesture identification by 12 %, when compared to the previous work (51 % in this study over 39 % in previous work). We successfully validate the proposed methods on 7 super classes (72 %), an ensemble of the 18 gestures/classes, and additional videos (75 %).

Abstract (translated)

URL

https://arxiv.org/abs/2205.11232

PDF

https://arxiv.org/pdf/2205.11232.pdf

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