An Efficient Audio Classification Approach Based on Support Vector Machines

Bahatti, Lhoussain; Bouattane, Omar; Echhibat, My Elhoussine; Zaggaf, Mohamed Hicham

doi:10.14569/ijacsa.2016.070530

Cited by 9 publications

(6 citation statements)

References 16 publications

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“…The reason for implementing this technique is to discriminate speech as well as music signals. Bahatti et al (2016) [9] carry out a study to increase the performances of audio classifications by focusing on the choice of efficient designations and relevant features.…”

Section: Related Workmentioning

confidence: 99%

Automatic Video Classification: A Review

Rani¹,

Kaur²,

Kaswan³

2020

EAI Endorsed Transactions on Creative Technologies

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INTRODUCTION: In last few years number of internet users and available bandwidth has been increased exponentially. The availability of internet with such a low cost is making audiovisual content a more popular and easier form of information exchange. The internet is having a huge amount of this audiovisual content and to classify and choose a particular type of video is becoming a difficult task. A number of video classification methods (like text, audio and video feature extraction) have been proposed by researcher's community OBJECTIVES: This work is carried out to give a review of different video classification techniques and give a comparative analysis of available video classification techniques and to suggest the most accurate and efficient method of video classification. METHODS: Text, Audio and Visual video classification techniques. RESULTS: It has been observed that a combination of audio and visual feature extraction can provide better results. CONCLUSION: There are various methods of video classification either by using text, audio or video extraction. The text feature extraction is the least used method of video classification. The audio and visual feature extraction is being used in various applications but as we can understand that both the audio and visual feature extractions are having equal importance in video feature extraction but if we use combination of both these approaches, the results in form of accuracy of video classification can be further improved.

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Section: Related Workmentioning

confidence: 99%

Automatic Video Classification: A Review

Rani¹,

Kaur²,

Kaswan³

2020

EAI Endorsed Transactions on Creative Technologies

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“…Between many methods, a consensus seems to have been formed through the use of Support Vector Machines (SVM) [17], [18], [19] due to their versatility, computational performance, the ability to handle high-dimensional data and the feature selection income. The model described in [20] is an Effective Audio Classification System based on Vector Support Machines designed to identify the composer. In [21], a system of the artificial composition of oriental music allowing to generate derived words based on the original pieces of a chosen composer.…”

Section: State Of the Artmentioning

confidence: 99%

“…The table below illustrates the information on the pieces used: We opted for a 5-second segmentation time for each piece after testing a set of segmentation times [2,20] to find the best choice.…”

Section: Process Implementationmentioning

confidence: 99%

Classification algorithms to recognize the influence of the playing style of the Oud masters on the musicians of the Oud instrument

Zhar¹

2020

IJATCSE

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In an effort to recognize the influence of the famous Oud masters on the style of the Oud player game, and for the aim to classify which Oud school, an Oud player belongs to, in this work, we are proposing a model for recognizing the influence of Oud Master Player based on extracted features using for multiple machine learning classification algorithms KNN, Decision Tree and SVM. The improvement brought about by this work is also based on the integration of a procedure for selecting the optimal characteristics. Practical cases were also developed in this work in order to test the relevance and efficiency of our model.

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“…These are called support vectors. SVMs were developed in the 1990s based on Vladimir Vapnik"s theoretical considerations on the development of a statistical theory of learning: the VapnikChervonenkis theory [15], [16].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Voice Detection in Traditionnal Tunisian Music using Audio Features and Supervised Learning Algorithms

Ziadi¹,

Amiri²

2018

ijacsa

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Abstract-The research presented in this paper aims to automatically detect the singing voice in traditional Tunisian music, taking into account the main characteristics of the sound of the voice in this particular music style. This means creating the possibility to automatically identify instrumental and singing sounds. Therefore different methods for the automatic classification of sounds using supervised learning algorithms were compared and evaluated. The research is divided into four successive stages. First, the extraction of features vectors from the audio tracks (through calculation of the parameters of sound perception) followed by the selection and transformation process of relevant features for singing/instrumental discrimination. Then, using learning algorithms, the instrumental and vocal classes were modeled from a manually annotated database. Finally, the evaluation of the decision-making process (indexing) was applied on the test part of the database. The musical databases used for this study consists of extracts from the national sound archives of Centre of Mediterranean and Arabic Music (CMAM) and recordings made especially for this research. The possibility to index audio data (classify/segment) into vocal and instrumental recognition allows for the retrieval of content-based information of musical databases.

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An Efficient Audio Classification Approach Based on Support Vector Machines

Cited by 9 publications

References 16 publications

Automatic Video Classification: A Review

Automatic Video Classification: A Review

Classification algorithms to recognize the influence of the playing style of the Oud masters on the musicians of the Oud instrument

Voice Detection in Traditionnal Tunisian Music using Audio Features and Supervised Learning Algorithms

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