Implementation of Neural Network Backpropagation Using Audio Feature Extraction For Classification Of Gamelan Notes

Firdausillah, Fahri; Mahendra, Dwiky Gilang; Zeniarja, Junta; Luthfiarta, Ardytha; Santoso, Heru Agus; Nugraha, Adhitya; Hidayat, Erwin Yudi; Syukur, Abdul

doi:10.1109/isemantic.2018.8549805

Cited by 4 publications

(3 citation statements)

References 3 publications

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“…The approach used for the feature classification process in this research is the backpropagation mechanism 55 . Backpropagation constitutes the central learning hub for producing an accurate classification outcome.…”

Section: Methodsmentioning

confidence: 99%

Android Permission Classifier: a deep learning algorithmic framework based on protection and threat levels

Ashawa

Morris

2021

Security and Privacy

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Recent works demonstrated that Android is the fastest growing mobile OS with the highest number of users worldwide. Android's popularity is facilitated by factors such as ease of use, open‐source, and cheap to purchase compared to mobile OS like iOS. The widespread of Android has brought an exponential increase in the complexity and number of malicious applications targeting Android. Malware deploys different attack vectors to exploit Android vulnerability and attack the OS. One way to thwart malware attacks on Android is the use of Android security patches, antivirus software, and layer security. However, the fact that the permission request dynamic is different from other attack vectors, makes it difficult to identify which permission request is malicious or not especially when constructing permission request profiles for Android users. The aforementioned challenge is tackled by our research. This article proposed a framework called Android Permission Classifier for the classification of Android malware permission requests based on threat levels. This article is the first to classify Android permission based on their protection and threat levels. With the framework, out of the 113 permissions extracted, 23 were classified as more dangerous. Our model shows classification accuracy of 97% and an FPR value of 0.2% with high diversity capacity when compared with the performance of those of other similar existing methods.

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Section: Methodsmentioning

confidence: 99%

Android Permission Classifier: a deep learning algorithmic framework based on protection and threat levels

Ashawa

Morris

2021

Security and Privacy

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“…Therefore, it undoubtedly forms an issue similar to the area explored in this article, as it concerns the broadly understood issues of classification related to music. The article [31] introduces a neural network-based backpropagation application using audio function extraction to classify gamelan notation. The study proposes an analysis based on the sonic classification of gamelan musical instruments.…”

Section: Related Workmentioning

confidence: 99%

The Study of the Effectiveness of Advanced Algorithms for Learning Neural Networks Based on FPGA in the Musical Notation Classification Task

Sokol¹,

Pawuś²,

Majewski³

et al. 2022

Applied Sciences

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The work contains an original comparison of selected algorithms using artificial neural network models, such as RBF neural networks, and classic algorithms, approaches that are based on structured programming in the image identification task. The existing studies exploring methods for the problem of classifying musical notation used in this work are still scarce. The research of neural network based and the classical method of image recognition was carried out on the basis of the effectiveness of recognizing the notes presented on the treble staff. In order to carry out the research, the density of the data distribution was modeled by means of the probabilistic principal component analysis, and a simple regression was performed with the use of a radial neural network. The methods of image acquisition and analysis are presented. The obtained results were successively tested in terms of selected quality criteria. The development of this research may contribute to supporting the learning of musical notation by both beginners and blind people. The further development of the experiments can provide a convenient reading of the musical notation with the help of a classification system. The research is also an introduction of new algorithms to further tests and projects in the field of music notation classification.

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“…There was another research using Backpropagation Neural Network as well for gamelan instruments [12]. Their gamelan instruments are gambang, kenong, gong and saron.…”

Section: Introductionmentioning

confidence: 99%

Gamelan Demung Music Transcription Based on STFT Using Deep Learning

Hermawan

Yuniarno²,

Wulandari³

2022

JAREE

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Learning to play a gamelan instrument would be easier when there’s a musical notation guide. The process of converting a musical signal into a notation guide is called transcription. In this paper, we would like to transcript the gamelan music especially the Demung instrument using the Deep Learning method. Each Demung’s note from 6-low until 1-high would be converted to the time-frequency domain using STFT (Short-Time Fourier Transform). Then, those data will be treated as an input for the multilayers perceptron. The training method is a single label of each notation. The output returned by the model is a music roll transcription.

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Implementation of Neural Network Backpropagation Using Audio Feature Extraction For Classification Of Gamelan Notes

Cited by 4 publications

References 3 publications

Android Permission Classifier: a deep learning algorithmic framework based on protection and threat levels

Android Permission Classifier: a deep learning algorithmic framework based on protection and threat levels

The Study of the Effectiveness of Advanced Algorithms for Learning Neural Networks Based on FPGA in the Musical Notation Classification Task

Gamelan Demung Music Transcription Based on STFT Using Deep Learning

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