Automatic Classification of Music Genre Using Masked Conditional Neural Networks

Medhat, Fady; Chesmore, David

doi:10.1109/icdm.2017.125

Cited by 10 publications

(5 citation statements)

References 17 publications

(33 reference statements)

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“…However, studies have been recently conducted on the classification of fluids sounds in other areas, including human heartbeat [41], urban sounds [42][43][44][45][46][47][48] play sounds, car horns, air conditioning sound, engine sounds, etc. ), and music [49][50][51][52][53][54][55][56], using deep learning. To the best of our knowledge, no article has been published in the field of classifying and identifying sounds created by fluids, by applying deep learning.…”

Section: Fig1 Venn Diagram Of Artificial Intelligencementioning

confidence: 99%

Fluid Temperature Detection Based on its Sound with a Deep Learning Approach

Yazdani¹,

Mehr²,

Showkatyan³

et al. 2021

IJIGSP

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The present study, the main idea of which was based on one of the questions of I.P.T.2018 competition, aimed to develop a high-precision relationship between the fluid temperature and the sound produced when colliding with different surfaces, by creating a data collection tool. In fact, this paper was provided based on a traditional phenomenological project using the well-known deep neural networks, in order to achieve an acceptable accuracy in this project. In order to improve the quality of the paper, the data were analyzed in two ways: I. Using the images of data spectrogram and the known V.G.G.16 network. II. Applying the data audio signal and a convolutional neural network (C.N.N.). Finally, both methods have obtained an acceptable precision above 85%.

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Section: Fig1 Venn Diagram Of Artificial Intelligencementioning

confidence: 99%

Fluid Temperature Detection Based on its Sound with a Deep Learning Approach

Yazdani¹,

Mehr²,

Showkatyan³

et al. 2021

IJIGSP

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“…For example, Choi et al (2017) introduced a convolutional recurrent neural network for music classification. Medhat et al (2017) designed a masked conditional neural network for multidimensional music classification.…”

Section: Music Style Classificationmentioning

confidence: 99%

Review-Driven Multi-Label Music Style Classification by Exploiting Style Correlations

Zhao

Zeng

et al. 2019

Proceedings of the 2019 Conference of the North

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This paper explores a new natural language processing task, review-driven multi-label music style classification. This task requires the system to identify multiple styles of music based on its reviews on websites. The biggest challenge lies in the complicated relations of music styles. It has brought failure to many multi-label classification methods. To tackle this problem, we propose a novel deep learning approach to automatically learn and exploit style correlations. The proposed method consists of two parts: a label-graph based neural network, and a soft training mechanism with correlation-based continuous label representation. Experimental results show that our approach achieves large improvements over the baselines on the proposed dataset. Especially, the micro F1 is improved from 53.9 to 64.5, and the one-error is reduced from 30.5 to 22.6. Furthermore, the visualized analysis shows that our approach performs well in capturing style correlations.

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“…The Gaussian mixture model [17] is a very efficient model used for the environmental sound source classification system based on Melfrequency cepstral coefficients. A masked neural networks technique was used to classify music genres [18]. Engine sound is one of the most important aspects of a vehicle.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Sound-Based Early Fault Detection System for Vehicles

Nasim¹,

Masood²,

Jaffar³

et al. 2023

Computer Systems Science and Engineering

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An intelligent sound-based early fault detection system has been proposed for vehicles using machine learning. The system is designed to detect faults in vehicles at an early stage by analyzing the sound emitted by the car. Early detection and correction of defects can improve the efficiency and life of the engine and other mechanical parts. The system uses a microphone to capture the sound emitted by the vehicle and a machine-learning algorithm to analyze the sound and detect faults. A possible fault is determined in the vehicle based on this processed sound. Binary classification is done at the first stage to differentiate between faulty and healthy cars. We collected noisy and normal sound samples of the car engine under normal and different abnormal conditions from multiple workshops and verified the data from experts. We used the time domain, frequency domain, and time-frequency domain features to detect the normal and abnormal conditions of the vehicle correctly. We used abnormal car data to classify it into fifteen other classical vehicle problems. We experimented with various signal processing techniques and presented the comparison results. In the detection and further problem classification, random forest showed the highest results of 97% and 92% with time-frequency features.

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Automatic Classification of Music Genre Using Masked Conditional Neural Networks

Cited by 10 publications

References 17 publications

Fluid Temperature Detection Based on its Sound with a Deep Learning Approach

Fluid Temperature Detection Based on its Sound with a Deep Learning Approach

Review-Driven Multi-Label Music Style Classification by Exploiting Style Correlations

Intelligent Sound-Based Early Fault Detection System for Vehicles

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