Distance Metric Learnt Kernel-Based Music Classification Using Timbral Descriptors

2021 ◽  
Author(s):  
Rajeev Rajan ◽  
B. S. Shajee Mohan
Keyword(s):  
Folk Music ◽  
Nearest Neighbor ◽  
Metric Learning ◽  
Support Vector ◽  
Distance Metric ◽  
K Nearest Neighbor ◽  
Genre Classification ◽  
Rbf Kernel ◽  
Music Genre ◽  
Music Genre Classification

Automatic music genre classification based on distance metric learning (DML) is proposed in this paper. Three types of timbral descriptors, namely, mel-frequency cepstral coefficient (MFCC) features, modified group delay features (MODGDF) and low-level timbral feature sets are combined at the feature level. We experimented with k nearest neighbor (kNN) and support vector machine (SVM)-based classifiers for standard and DML kernels (DMLK) using GTZAN and Folk music dataset. Standard kernel-based kNN and SVM-based classifiers report classification accuracy (in%) of 79.03 and 90.16, respectively, on GTZAN dataset and 86.60 and 92.26, respectively, for Folk music dataset, with the best performing RBF kernel. A further improvement was observed when DML kernels were used in place of standard kernels in the kernel kNN and SVM-based classifiers with an accuracy of 84.46%, 92.74% (GTZAN), 90.00 and 96.23 (Folk music dataset) for DMLK-kNN and DMLK-SVM, respectively. The results demonstrate the potential of DML kernels in music genre classification task.

scholarly journals Indonesian Music Classification on Folk and Dangdut Genre Based on Rolloff Spectral Feature Using Support Vector Machine (SVM) Algorithm

2021 ◽  
Vol 15 (1) ◽  
pp. 11
Author(s):  
Brizky Ramadhani Ismanto ◽  
Tubagus Maulana Kusuma ◽  
Dina Anggraini
Keyword(s):  
Support Vector Machine ◽  
Folk Music ◽  
Spectral Feature ◽  
Folk Songs ◽  
Support Vector ◽  
Music Classification ◽  
Genre Classification ◽  
Svm Algorithm ◽  
Music Genre ◽  
Music Genre Classification

Music Genre Classification is one of the interesting digital music processing topics. Genre is a category of artistry, in this case, especially music, to characterize and categorize music is now available in various forms and sources. One of the applications is in determining the music genre classification on folk songs and dangdut songs.The main problem in the classification music genre is to find a combination of features and classifiers that can provide the best result in classifying music files into music genres. So we need to develop methods and algorithms that can classify genres appropriately. This problem can be solved by using the Support Vector Machine (SVM). The genre classification process begins by selecting the song file that will be classified by the genre, then the preprocessing process, the collection features by utilizing feature extraction, and the last process is Support Vector Machine (SVM) classification process to produce genre types from selected song files. The final result of this research is to classify Indonesian folk music genre and dangdut music genre along with the 83.3% accuracy values that indicate the level of system relevance to the results of music genre classification and to provide genre labels on music files as to facilitate the management and search of music files.

scholarly journals Using Machine Learning to Classify Music Genre

2021 ◽  
Vol 9 (10) ◽  
pp. 39-44
Author(s):  
Rachaell Nihalaani
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Art Form ◽  
Genre Classification ◽  
Machine Learning Model ◽  
Nearest Neighbours ◽  
Music Genre ◽  
The Mind ◽  
Music Genre Classification

Abstract: As Plato once rightfully said, ‘Music gives a soul to the universe, wings to the mind, flight to the imagination and life to everything.’ Music has always been an important art form, and more so in today’s science-driven world. Music genre classification paves the way for other applications such as music recommender models. Several approaches could be used to classify music genres. In this literature, we aimed to build a machine learning model to classify the genre of an input audio file using 8 machine learning algorithms and determine which algorithm is the best suitable for genre classification. We have obtained an accuracy of 91% using the XGBoost algorithm. Keywords: Machine Learning, Music Genre Classification, Decision Trees, K Nearest Neighbours, Logistic regression, Naïve Bayes, Neural Networks, Random Forest, Support Vector Machine, XGBoost

scholarly journals Dissimilarity space reinforced with manifold learning and latent space modeling for improved pattern classification

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Azadeh Rezazadeh Hamedani ◽  
Mohammad Hossein Moattar ◽  
Yahya Forghani
Keyword(s):  
Manifold Learning ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Geodesic Distance ◽  
Locally Linear Embedding ◽  
Support Vector ◽  
Distance Metric ◽  
K Nearest Neighbor ◽  
Latent Space ◽  
Space Modeling

AbstractDissimilarity representation plays a very important role in pattern recognition due to its ability to capture structural and relational information between samples. Dissimilarity space embedding is an approach in which each sample is represented as a vector based on its dissimilarity to some other samples called prototypes. However, lack of neighborhood-preserving, fixed and usually considerable prototype set for all training samples cause low classification accuracy and high computational complexity. To address these challenges, our proposed method creates dissimilarity space considering the neighbors of each data point on the manifold. For this purpose, Locally Linear Embedding (LLE) is used as an unsupervised manifold learning algorithm. The only goal of this step is to learn the global structure and the neighborhood of data on the manifold and mapping or dimension reduction is not performed. In order to create the dissimilarity space, each sample is compared only with its prototype set including its k-nearest neighbors on the manifold using the geodesic distance metric. Geodesic distance metric is used for the structure preserving and is computed using the weighted LLE neighborhood graph. Finally, Latent Space Model (LSM), is applied to reduce the dimensions of the Euclidean latent space so that the second challenge is resolved. To evaluate the resulted representation ad so called dissimilarity space, two common classifiers namely K Nearest Neighbor (KNN) and Support Vector Machine (SVM) are applied. Experiments on different datasets which included both Euclidean and non-Euclidean spaces, demonstrate that using the proposed approach, classifiers outperform the other basic dissimilarity spaces in both accuracy and runtime.

EPILEPTIC EEG CLASSIFICATION USING NONLINEAR PARAMETERS ON DIFFERENT FREQUENCY BANDS

2015 ◽  
Vol 15 (03) ◽  
pp. 1550040 ◽  
Author(s):  
ROSHAN JOY MARTIS ◽  
JEN HONG TAN ◽  
CHUA KUANG CHUA ◽  
TOO CHEAH LOON ◽  
SHARON WAN JIE YEO ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Linear Models ◽  
Kernel Functions ◽  
Support Vector ◽  
Largest Lyapunov Exponent ◽  
Brain Anatomy ◽  
K Nearest Neighbor ◽  
Rbf Kernel ◽  
Nonlinear Features ◽  
Non Gaussian

Epilepsy is a chronic neurological disorder with considerable incidence and affects the population everywhere in the world. It occurs due to recurrent unprovoked seizures which can be noninvasively diagnosed using electroencephalograms (EEGs) which are the neuronal electrical activity recorded on the scalp. The EEG signal is highly random, nonlinear, nonstationary and non-Gaussian in nature. The nonlinear features characterize the EEG more accurately than linear models. EEG comprsises of different activities like delta, theta, lower alpha, upper alpha, lower beta, upper beta and lower gamma which are correlated to the brain anatomy and its function. In the current study, the nonlinear features such as Hurst exponent (HE), Higuchi fractal dimension (HFD), largest Lyapunov exponent (LLE) and sample entropy (SE) are extracted on these individual activities to provide improved discrimination. The ranked features are classified using support vector machine (SVM) with different kernel functions, decision tree (DT) and k-nearest neighbor (KNN) to select the best classifier. It is observed that SVM with radial basis function (RBF) kernel provides highest accuracy of 98%, sensitivity and specificity of 99.5% and 100%, respectively using five features. The developed methodology is ready for epilepsy screening and can be deployed in many programmes.

The Effect of Feature Selection on Music Genre Classification

2021 ◽  
Vol 9 (4) ◽  
pp. 549
Author(s):  
I Nyoman Yusha Tresnatama Giri ◽  
Luh Arida Ayu Rahning Putri
Keyword(s):  
Feature Selection ◽  
Nearest Neighbor ◽  
Pearson Correlation ◽  
Feature Selection Method ◽  
K Nearest Neighbor ◽  
Genre Classification ◽  
Average Accuracy ◽  
Independent Variable ◽  
Music Genre Classification ◽  
Selection Of

One of the things that affects classification results is the correlation of features to the class of a data. This research was conducted to determine the effect of the reduction of features (independent variable) that have the weakest correlation or have a distant relationship with the class (dependent variable). Bivariate Pearson Correlation is used as a feature selection method and K-Nearest Neighbor is used as a classification method. Results of the test showing that, 75.1% average accuracy was obtained for classification without feature selection, while using feature selection, average accuracy was obtained in the range of 75% - 79.3%. The average accuracy obtained by the selection of features tends to be higher compared to the accuracy obtained without selection of features.

Sign in / Sign up

Export Citation Format

Share Document