Spectral-Temporal Receptive Field-Based Descriptors and Hierarchical Cascade Deep Belief Network for Guitar Playing Technique Classification
Journal
IEEE Transactions on Cybernetics
Journal Volume
52
Journal Issue
5
Pages
3684-3695
Date Issued
2022
Author(s)
Abstract
Music information retrieval is of great interest in audio signal processing. However, relatively little attention has been paid to the playing techniques of musical instruments. This work proposes an automatic system for classifying guitar playing techniques (GPTs). Automatic classification for GPTs is challenging because some playing techniques differ only slightly from others. This work presents a new framework for GPT classification: it uses a new feature extraction method based on spectral-temporal receptive fields (STRFs) to extract features from guitar sounds. This work applies a supervised deep learning approach to classify GPTs. Specifically, a new deep learning model, called the hierarchical cascade deep belief network (HCDBN), is proposed to perform automatic GPT classification. Several simulations were performed and the datasets of: 1) data on onsets of signals; 2) complete audio signals; and 3) audio signals in a real-world environment are adopted to compare the performance. The proposed system improves upon the F-score by approximately 11.47% in setup 1) and yields an F-score of 96.82% in setup 2). The results in setup 3) demonstrate that the proposed system also works well in a real-world environment. These results show that the proposed system is robust and has very high accuracy in automatic GPT classification. © 2013 IEEE.
Subjects
Deep belief network (DBN); guitar playing technique (GPT) classification; neural network; spectral-temporal receptive fields (STRFs)
Other Subjects
Audio acoustics; Audio signal processing; Deep learning; Music; Musical instruments; Audio signal; Deep belief network; Deep belief networks; F-score; Guitar playing technique classification; Neural-networks; Playing techniques; Real world environments; Receptive fields; Spectral-temporal receptive field; Classification (of information); music; signal processing; Music; Neural Networks, Computer; Signal Processing, Computer-Assisted
Type
journal article