Classification of Indian Classical Music With Time-Series Matching Deep Learning Approach

Sharma, Akhilesh; Aggarwal, Gaurav; Bhardwaj, Sachit; Chakrabarti, Prąsun; Chakrabarti, Tulika; Abawajy, Jemal; Bhattacharyya, Siddhartha; Mishra, Ramesh Kumar; Das, Anirban; Mahdin, Hairulnizam

doi:10.1109/access.2021.3093911

Cited by 73 publications

(29 citation statements)

References 23 publications

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“…And the loss function is to blame for this issue. An optimizer's guide is the loss function, which tells it whether it's heading in the right or incorrect path [48]. For example, the squared error function and the cross entropy loss function are two examples of loss functions.…”

Section: C: Loss Functionmentioning

confidence: 99%

BMNet-5: A Novel Approach of Neural Network to Classify the Genre of Bengali Music Based on Audio Features

et al. 2022

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Music genre classification (MGC) is the process of putting genre labels on music by analyzing the sounds or words. With the rapid growth of music data repositories, MGC can be used in a lot of ways to organize and manage music recommendation systems, advertising, and streaming services. But there have been a lot of works on classifying English music using different statistical and machine learning methods, but there hasn't been much progress in classifying Bengali music. Also, Deep Learning (DL) methods have been used in a few important ways to classify different types of music. The content and uniqueness of Bengali music make it much more interesting. Also, there is still a lot to learn about how to use the DL approach in Bengali music. So, Bengali music genre classification is a pretty new area of research in the field of Deep Learning. In this paper, we developed a unique technique called BMNet-5 to perform a multiclass classification of Bangla music genres such as "Bangla Adhunik," "Bangla Hip-Hop," "Bangla Band Music," "Nazrulgeeti," "Palligeeti," and "Rabindra Sangeet." We show the effectiveness of the suggested technique by extracting features from a dataset of 1742 Bangla music pieces and evaluating the automated classification judgments. The proposed BMNet-5 is based on a neural network designed to predict music genre from audio inputs. Our suggested model outperformed the corresponding previous research with an accuracy of 90.32%. The BMNet-5 model is then tested for performance consistency using K-fold cross validation with varying k values. Finally, we use the suggested model to train the interpretable SHAP model for all the genre of the Bangla music dataset, and the development of an explainable outcome may have a significant advantage.

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Section: C: Loss Functionmentioning

confidence: 99%

BMNet-5: A Novel Approach of Neural Network to Classify the Genre of Bengali Music Based on Audio Features

et al. 2022

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“…Some researchers Conducted to solve these problems. [23] There is a growing desire to reduce the operating costs of commercial aircraft. Built-in health monitor the practical application of the technologies is expected Because of aircraft SHM Technologies Maintenance costs and have the potential to reduce maintenance idle time.…”

Section: Structural Health Monitoringmentioning

confidence: 99%

A Study on Aircraft Structure and Application of Static Force

Vimala¹,

Ramachandran²,

Raja³

2022

jame

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In particular, the neural network approach to Demister-Schaefer theory has been demonstrated. Different modulus adherents, so that the adhesive pressures are approximately the same part of the vertical stabilizer was selected and reconstructed using the structure design of the hybrid wing body plane during the preliminary design phase. Due to the high number of design variables and various repairs, adhesive bonded fiber-composite joints or reinforcements are a challenging optimization problem, and this sheet directly affects structurally efficient and minimal damage by lightning strikes on aircraft structures. Parent systems rather than standard repairs. In the first part, the phenomenon of lightning curve connection in aircraft was introduced. Certain bonded joints offer more effective repairs than conventional mechanical joints, which are not fully credited with reducing their performance, and have important needs in most countries of the world. This article focuses on introducing integrated structural features and background, including application for civil aviation and flight qualification requirements, and the main purpose of this study is a computer-assisted materials selection tool. - Reinforced alloy. This paper explores the distribution of dynamic load identification given the unknown but limited uncertainties in aircraft configuration. This is the second of two documents on the structure of the Northwest Pacific Hurricane This sheet describes various structures Thin metal sheets are widely used to create different types of space structures due to its flexibility and ability to easily create any type of structure. Resource based to identify damage this article discusses the use of taxonomies.

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“…Sharma et al argued that neural networks can classify and identify music morphology. Based on classical music, they found a better classification model by comparing several network models [ 7 ]. The classification of music morphology needs to extract the features of music works.…”

Section: Introductionmentioning

confidence: 99%

Music Morphology Interaction under Artificial Intelligence in Wireless Network Environment

Zhang

2022

Computational Intelligence and Neuroscience

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Music has become the main information carrier, and music and its emotional expression are accurately classified to obtain relevant information. However, how to classify music accurately is a problem that needs to be discussed. The concept and feature extraction strategy of the morphology of music are described. Moreover, the feature extraction and morphological classification elements of digital music are introduced. Next, music morphology is recognized and classified based on the neural network and relief algorithm. In the network, by randomly selecting different music types, the audio data is input into the neural network as the original data and processed by the relief algorithm. The classification and recognition accuracy of the Relief algorithm are verified by changing the number of iterations. The results show that the model’s classification accuracy based on the number of iterations is 78.958%. Then, the traditional statistical analysis classification method’s performance is compared with the proposed model. The recognition accuracy of the model proposed reaches 92%, which shows that the model can effectively classify music morphology. This study provides a theoretical basis for music morphology recognition in the wireless network environment.

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Classification of Indian Classical Music With Time-Series Matching Deep Learning Approach

Cited by 73 publications

References 23 publications

BMNet-5: A Novel Approach of Neural Network to Classify the Genre of Bengali Music Based on Audio Features

BMNet-5: A Novel Approach of Neural Network to Classify the Genre of Bengali Music Based on Audio Features

A Study on Aircraft Structure and Application of Static Force

Music Morphology Interaction under Artificial Intelligence in Wireless Network Environment

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