Review Techniques for Vocal Classification using Machine Learning

Author Reg. ID: TIJER_106708

Published Paper Id: TIJERA001045

Published In: Volume 10 Issue 7, July-2023

DOI: http://doi.one/10.1729/Journal.35434

Abstract: This review paper provides a comprehensive overview of vocal classification using machine learning techniques. This paper analyzes and compares different approaches for vocal classification, including traditional and deep learning techniques. This paper discusses various datasets used in vocal classification research and their properties. Furthermore, the paper provides a comparative analysis of existing studies in this field. Finally, the paper highlights the future scope of vocal classification using machine learning techniques and suggests further research opportunities in this area. Overall, this review paper aims to provide a valuable resource for researchers and practitioners working with vocal classification..

Keywords: Machine learning (ML), Vocal classification (VC), Audio analysis (AA), Feature extraction (FE), Signal processing (SP), Pattern recognition (PR), Deep learning (DL), Convolutional neural networks (CNN), Support vector machines (SVM), Random forests (RF), K-nearest neighbors (KNN), Gaussian mixture models (GMM), Performance evaluation (PE), Dataset (DS), Preprocessing (PP), Feature selection (FS), Spectrogram (SG), Mel-frequency cepstral coefficients (MFCC), Pitch detection (PD), Formant estimation (FE), Voice quality (VQ).

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Page No: 261-267

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Research Area: Science and Technology

Published Paper URL: https://tijer.org/TIJER/viewpaperforall?paper=TIJERA001045

Published Paper PDF: https://tijer.org/TIJER/papers/TIJERA001045

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