Version 1
: Received: 5 May 2021 / Approved: 7 May 2021 / Online: 7 May 2021 (09:09:14 CEST)
How to cite:
Sinha, A.; soni, H. Segmentation and Classification of Beatboxing Acoustic Voice Tract Variations in MRI through Image Processing Technique. Preprints2021, 2021050127. https://doi.org/10.20944/preprints202105.0127.v1
Sinha, A.; soni, H. Segmentation and Classification of Beatboxing Acoustic Voice Tract Variations in MRI through Image Processing Technique. Preprints 2021, 2021050127. https://doi.org/10.20944/preprints202105.0127.v1
Sinha, A.; soni, H. Segmentation and Classification of Beatboxing Acoustic Voice Tract Variations in MRI through Image Processing Technique. Preprints2021, 2021050127. https://doi.org/10.20944/preprints202105.0127.v1
APA Style
Sinha, A., & soni, H. (2021). Segmentation and Classification of Beatboxing Acoustic Voice Tract Variations in MRI through Image Processing Technique. Preprints. https://doi.org/10.20944/preprints202105.0127.v1
Chicago/Turabian Style
Sinha, A. and Harsh soni. 2021 "Segmentation and Classification of Beatboxing Acoustic Voice Tract Variations in MRI through Image Processing Technique" Preprints. https://doi.org/10.20944/preprints202105.0127.v1
Abstract
Human beatboxing is a vocal art making use of speech organs to produce vocal drum sounds and imitate musical instruments. Beatbox sound classification is a current challenge that can be used for automatic database annotation and music-information retrieval. In this study, a large-vocabulary humanbeatbox sound recognition system was developed with an adaptation of Kaldi toolbox, a widely-used tool for automatic speech recognition. The corpus consisted of eighty boxemes, which were recorded repeatedly by two beatboxers. The sounds were annotated and transcribed to the system by means of a beatbox specific morphographic writing system (Vocal Grammatics). The image processing techniques plays vital role on image Acquisition, image pre-processing, Clustering, Segmentation and Classification techniques with different kind of images such as Fruits, Medical, Vehicle and Digital text images etc. In this study the various images to remove unwanted noise and performs enhancement techniques such as contrast limited adaptive histogram equalization, Laplacian and Harr filtering, unsharp masking, sharpening, high boost filtering and color models then the Clustering algorithms are useful for data logically and extract pattern analysis, grouping, decision-making, and machine-learning techniques and Segment the regions using binary, K-means and OTSU segmentation algorithm. It Classifying the images with the help of SVM and K-Nearest Neighbour(KNN) Classifier to produce good results for those images.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
