Speech/music classification using PLP and SVM

R, Thiruven Gatanadhan

doi:10.18535/ijecs.v8i02.4277

Cited by 2 publications

(1 citation statement)

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“…Different audio signal processing methods would obtain different features. The proposed feature extraction and classification model provides higher accuracy in music classification [19]. The early development of musical features underwent three stages of classification.…”

Section: B Audio Feature Selection and Evaluationmentioning

confidence: 99%

PMG-Net: Electronic Music Genre Classification using Deep Neural Networks

Tang

2023

IJACSA

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With the rapid development of electronic music industry, how to establish a set of electronic music genre automatic classification technology has also become an urgent problem. This paper utilized neural network (NN) technology to classify electronic music genres. The basic idea of the research was to establish a deep neural network (DNN) based classification model to analyze the audio signal processing and classification feature extraction of electronic music. In this paper, 2700 different types of electronic music were selected as experimental data from the publicly available dataset of W website, and substituted into the convolutional neural network (CNN) model, PMG-Net electronic music genre classification model and traditional classification model for comparison. The results showed that the PMG-Net model had the best classification performance and the highest recognition accuracy. The classification error of PMG-Net electronic music genre classification model in each round of training was smaller than the other two classification models, and the fluctuation was small. The speed of music signal processing in each round and the feature extraction of audio samples of PMG-Net electronic music genre classification model were faster than the traditional classification model and CNN model. It can be seen that using the PMG-Net electronic music genre classification model customized based on DNN for automatic classification of electronic music genres has a better classification effect, and can achieve the goal of efficiently completing the classification in massive data.

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Section: B Audio Feature Selection and Evaluationmentioning

confidence: 99%

PMG-Net: Electronic Music Genre Classification using Deep Neural Networks

Tang

2023

IJACSA

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Emotion Recognition from Speech using SVM and Random Forest Classifier

Annal¹,

Manonmani²,

Booma³

2022

JSCP

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Speech is the most natural way of people to communicate with one another. It is a vital medium for communicating a person's thoughts, feelings, and mental condition to others. The process of identifying the intellectual state is the recognition of basic emotion through speech. In human life, emotions are incredibly significant. In this project, the emotion is recognized from speech using Support Vector Machine (SVM) and Random Forest classifiers. These are supervised machine learning algorithms used for both classification and regression problems. SVM classifies data by creating N-dimensional hyper planes that divide the input into different categories. The classification is accomplished using a linear and non-linear separation surface in the dataset's input feature. Random Forest is a classifier that combines a number of decision trees on different subsets of a dataset and averages the results to increase the dataset's predicted accuracy. These classifiers are used to categorize emotions like happiness, rage, sadness and neutral for a certain incoming voice signal. Here, the system is trained and developed to recognize emotion in real-time speech. The result demonstrates that the Random Forest classifier is significantly better, when compared to the SVM classifier.

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Speech/music classification using PLP and SVM

Cited by 2 publications

References 5 publications

PMG-Net: Electronic Music Genre Classification using Deep Neural Networks

PMG-Net: Electronic Music Genre Classification using Deep Neural Networks

Emotion Recognition from Speech using SVM and Random Forest Classifier

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