An Optimization of Audio Classification and Segmentation using GASOM Algorithm

Karim, Dabbabi; Cherif, Adnane; Salah, Hajji

doi:10.14569/ijacsa.2018.090424

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…Cross validation such as accuracy, precision, recall, and F-measure are used to check the accuracy of the CFLB. The classifiers such as ACO-SVM [97], SVM-GA [98], GASOM [99], and KNSC [100] are used to perform comparative analysis given in Tab. 5.…”

Section: Accuracy Of Cflbmentioning

confidence: 99%

Enhancing Cloud Performance Using File Format Classifications

Junaid¹,

Sohail²,

Alkinani³

et al. 2022

Computers, Materials &Amp; Continua

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Metaheuristic approaches in cloud computing have shown significant results due to their multi-objective advantages. These approaches are now considering hybrid metaheuristics combining the relative optimized benefits of two or more algorithms resulting in the least tradeoffs among several factors. The critical factors such as execution time, throughput time, response time, energy consumption, SLA violations, communication overhead, makespan, and migration time need careful attention while designing such dynamic algorithms. To improve such factors, an optimized multi-objective hybrid algorithm is being proposed that combines the relative advantages of Cat Swarm Optimization (CSO) with machine learning classifiers such as Support Vector Machine (SVM). The adopted approach is based on SVM one to many classification models of machine learning that performs the classifications of various data format types in the cloud with best accuracy. In CSO, grouping phase is used to divide the data files as audio, video, image, and text which is further extended by polynomial Kernel function based on various input features and used for optimized load balancing. Overall, proposed approach works well and achieved performance efficiency in evaluated QoS metrics such as average energy consumption by 12%, migration time by 9%, and optimization time by 10%, in the presence of competitor baselines.

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Section: Accuracy Of Cflbmentioning

confidence: 99%

Enhancing Cloud Performance Using File Format Classifications

Junaid¹,

Sohail²,

Alkinani³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…A method for determining the maximum cross-correlation value between two audio files and a subsequent automatic segmentation method are described in order to extract two valid sound samples of target consonants, with the aim of preprocessing the audio file and feeding the evenly trimmed audio samples to a computerized SSD screening system. Dabbabi Karim et al [5] propose an optimized audio classification and segmentation algorithm for dividing stacked audio streams into 10 main audio types based on their content. They tested KNN, SVM and GASOM algorithms on two audio classification systems.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Research on Algorithm of Video Analysis System Based on Text Error Correction

Wang¹,

Qin²,

Shi³

et al. 2023

FCIS

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When making a video, if the video has a language organization error, it needs to be re-recorded. It is not possible to remove inappropriate or unnatural pronunciation parts of the recording more effectively. In response to this problem, this paper studies the speech extraction, error correction and synthesis of video, which is divided into three parts: (1) Speech segmentation and speech-to-text of video; (2) Text recognition error correction; (3) Text-to-speech and video speech synthesis. For the first part, we applied the staged and efficient algorithm based on (Bayesian Information Criterion) BIC & (Statistical Mean Euclidean Distance) MEdist to segment the video voice, and then, the segmented audio is subtracted to reduce noise, and finally converted to text using the iFLYTEK interface. For the second part, we apply the (Double Automatic Error Correction) DAEC algorithm to text error correction. For the third part, we use the (Improved Chinese Realtime Voice Cloning) I-Zhrtvc for text-to-speech. Then merge the voice into the video. The simulation result shows that the staged and efficient algorithm based on BIC & MEdist, which accurately segmented by sentences, can identify audio with dialect accents, and has high accuracy in translating to text, up to an average of 95.8%. DAEC algorithm has a high error correction rate. The audio prosody accuracy after synthesis is high. ZVTOW text-to-speech (Mean Opinion Score) MOS up to 4.5.

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“…It was found to be a suitable way to reduce computational load even further for DTMF detection. Dabbabi Karim et al [14] presented a new method to optimize audio classification and segmentation by utilizing the Genetic Algorithm using Self-Orga-nizing Maps (GASOM) algorithm for their multimedia data set. Audio coding using empirical mode decomposition has been presented in [15].…”

Section: Introductionmentioning

confidence: 99%

Robust dual-tone multi-frequency tone detection using k-nearest neighbour classifier for a noisy environment

Maity

Periasamy

Bhargava

2021

ACI

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PurposeDue to the continuous and rapid evolution of telecommunication equipment, the demand for more efficient and noise-robust detection of dual-tone multi-frequency (DTMF) signals is most significant.Design/methodology/approachA novel machine learning-based approach to detect DTMF tones affected by noise, frequency and time variations by employing the k-nearest neighbour (KNN) algorithm is proposed. The features required for training the proposed KNN classifier are extracted using Goertzel's algorithm that estimates the absolute discrete Fourier transform (DFT) coefficient values for the fundamental DTMF frequencies with or without considering their second harmonic frequencies. The proposed KNN classifier model is configured in four different manners which differ in being trained with or without augmented data, as well as, with or without the inclusion of second harmonic frequency DFT coefficient values as features.FindingsIt is found that the model which is trained using the augmented data set and additionally includes the absolute DFT values of the second harmonic frequency values for the eight fundamental DTMF frequencies as the features, achieved the best performance with a macro classification F1 score of 0.980835, a five-fold stratified cross-validation accuracy of 98.47% and test data set detection accuracy of 98.1053%.Originality/valueThe generated DTMF signal has been classified and detected using the proposed KNN classifier which utilizes the DFT coefficient along with second harmonic frequencies for better classification. Additionally, the proposed KNN classifier has been compared with existing models to ascertain its superiority and proclaim its state-of-the-art performance.

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An Optimization of Audio Classification and Segmentation using GASOM Algorithm

Cited by 5 publications

References 23 publications

Enhancing Cloud Performance Using File Format Classifications

Enhancing Cloud Performance Using File Format Classifications

Research on Algorithm of Video Analysis System Based on Text Error Correction

Robust dual-tone multi-frequency tone detection using k-nearest neighbour classifier for a noisy environment

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