scholarly journals Music Genre Classification Using Deep Learning with KNN

2021 ◽  
pp. 224-230
Author(s):  
Dr. S. Ponlatha ◽  
Mathisalini B ◽  
Deepthisri K. A ◽  
Kalaiyarasi. M ◽  
Kowshika. V
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Machine Learning Techniques ◽  
Music Score ◽  
Genre Classification ◽  
Learning Techniques ◽  
Music Genre ◽  
The Right ◽  
Music Streaming ◽  
Music Genre Classification

Music genre is a conventional category that predicts the genre of music belonging to tradition or set of conventions. A music platform, with total assets of $26 billion, is ruling the music streaming stage today. At present, it has a huge number of tunes and it is information base and claims to have the right music score for everybody. Like, Spotify, Amazon music, Wynk has put a great deal in examination to further develop the manner in which clients find and pay attention to music. AI is at the centre of their examination. From NLP to Collaborative sifting to Deep Learning, All music platforms utilizes them all. Tunes are examined dependent on their advanced marks for certain elements, including rhythm, acoustics, energy, danceability, and so forth, to answer that incomprehensible old first-date inquiry. Organizations these days use music arrangement, either to have the option to put suggestions to their clients (like Spotify, Soundcloud) or just as an item (for instance, Shazam). Deciding music sorts is the initial phase toward that path. AI procedures have ended up being very fruitful in removing patterns and examples from a huge information pool. Similar standards are applied in Music Analysis moreover. Machine learning techniques are achieved in some recent years and rarely in deep learning. Most of the current music genre classification uses Machine learning techniques. In this, we present a music dataset which includes many genres like Rock, Pop, folk, Classical and many genres. A Deep learning approach is used in order to train and classify the system using KNN.

scholarly journals Music Genre Classification using Deep Learning

2021 ◽  
Vol 9 (VII) ◽  
pp. 66-71
Author(s):  
Sheeba Fathima
Keyword(s):  
Neural Networks ◽  
Machine Learning Techniques ◽  
Data Set ◽  
Music Production ◽  
Genre Classification ◽  
Learning Techniques ◽  
Network Topologies ◽  
Classification Tasks ◽  
Music Genre ◽  
Music Genre Classification

Many subjects are affected by digital music production., including music genre prediction. Machine learning techniques were used to classify music genres in this research. Deep neural networks (DNN) have recently been demonstrated to be effective in a variety of classification tasks. Including music genre classification. In this paper, we propose two methods for boosting music genre classification with convolutional neural networks: 1) using a process inspired by residual learning to combine peak- and average pooling to provide more statistical information to higher level neural networks; and 2) To bypass one or more layers, use shortcut connections. To perform classification, the KNN output is fed into another deep neural network. Our preliminary experimental results on the GTZAN data set show that the above two methods, especially the second one, can effectively improve classification accuracy when compared to two different network topologies.

scholarly journals Music Genre Classification using Optimized Sequential Neural Network

2021 ◽  
Vol 10 (3) ◽  
pp. 1949-1958
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Learning Approaches ◽  
Human Beings ◽  
Advantages And Disadvantages ◽  
Genre Classification ◽  
Music Genre ◽  
Music Genre Classification

Music makes up a huge portion of the contents stored and used over the internet, with several sites and applications developed solely to provide music-related services to their users/ customers.Some of the most challenging tasks in this scenario would include music classification based on languages and genres, playlist suggestions based on music history, song suggestions based on playlist contents, top genres / songs based on listeners' rating, likes, number of streams, song loops, popularity of artists based on number of songs released per year, hit songs per year, etc. One of the most important stages to solve the above-mentioned challenges would be music genre classification. It would be impractical to analyze each and every song in a given database to identify and classify music genres, even though human beings are better at performing such tasks. Hence, useful Machine Learning algorithms and Deep Learning approaches may be used for accomplishing such tasks with ease. A thorough analysis to understand the different uses of Machine Learning and Deep Learning algorithms and relevance of such algorithms with respect to situations would be made to highlight and contrast the advantages and disadvantages of each approach. The outcomes of the optimized models would be visualized and comparedto the expected outcomes for better perception.

scholarly journals Detection and Severity Evaluation of Combined Rail Defects Using Deep Learning

Vibration ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 341-356
Author(s):  
Jessada Sresakoolchai ◽  
Sakdirat Kaewunruen
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Machine Learning Techniques ◽  
Rolling Stock ◽  
Raw Data ◽  
Learning Techniques ◽  
Combined Defects

Various techniques have been developed to detect railway defects. One of the popular techniques is machine learning. This unprecedented study applies deep learning, which is a branch of machine learning techniques, to detect and evaluate the severity of rail combined defects. The combined defects in the study are settlement and dipped joint. Features used to detect and evaluate the severity of combined defects are axle box accelerations simulated using a verified rolling stock dynamic behavior simulation called D-Track. A total of 1650 simulations are run to generate numerical data. Deep learning techniques used in the study are deep neural network (DNN), convolutional neural network (CNN), and recurrent neural network (RNN). Simulated data are used in two ways: simplified data and raw data. Simplified data are used to develop the DNN model, while raw data are used to develop the CNN and RNN model. For simplified data, features are extracted from raw data, which are the weight of rolling stock, the speed of rolling stock, and three peak and bottom accelerations from two wheels of rolling stock. In total, there are 14 features used as simplified data for developing the DNN model. For raw data, time-domain accelerations are used directly to develop the CNN and RNN models without processing and data extraction. Hyperparameter tuning is performed to ensure that the performance of each model is optimized. Grid search is used for performing hyperparameter tuning. To detect the combined defects, the study proposes two approaches. The first approach uses one model to detect settlement and dipped joint, and the second approach uses two models to detect settlement and dipped joint separately. The results show that the CNN models of both approaches provide the same accuracy of 99%, so one model is good enough to detect settlement and dipped joint. To evaluate the severity of the combined defects, the study applies classification and regression concepts. Classification is used to evaluate the severity by categorizing defects into light, medium, and severe classes, and regression is used to estimate the size of defects. From the study, the CNN model is suitable for evaluating dipped joint severity with an accuracy of 84% and mean absolute error (MAE) of 1.25 mm, and the RNN model is suitable for evaluating settlement severity with an accuracy of 99% and mean absolute error (MAE) of 1.58 mm.

Automatic Music Genre Classification Based on Linguistic Frequencies Using Machine Learning

2021 ◽  
Author(s):  
Mekala Srinivasa Rao ◽  
O. Pavan Kalyan ◽  
N. Naresh Kumar ◽  
Md. Tasleem Tabassum ◽  
B. Srihari
Keyword(s):  
Machine Learning ◽  
Genre Classification ◽  
Music Genre ◽  
Music Genre Classification
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