Real-Time Implementation of Speaker Diarization System on Raspberry PI3 Using TLBO Clustering Algorithm

Karim, Dabbabi; Hajji, Salah; Cherif, Adnane

doi:10.1007/s00034-020-01357-2

Cited by 4 publications

(2 citation statements)

References 45 publications

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“…This section describes the performance analysis of the proposed SDS‐HXLP‐DCNN‐SOA speaker diarization scheme depending on metrics, namely, tracking distance, FAR, DER. The performance is likened to the existing methods, such as real‐time implementation of speaker diarization system on Raspberry PI3 utilizing TLBO clustering algorithm (SDS‐RPi3‐TLBO), 35 deep self‐supervised hierarchical clustering for speaker diarization (SDS‐TDNN), 36 meta‐learning with latent space clustering in generative adversarial network for speaker diarization (SDS‐MCGAN), 37 A new method for speaker diarization system utilizing TMFCC parameterization with lion optimization (SDS‐TMFCC‐DNN‐LOA), 38 speaker diarization system utilizing HXLPS with deep neural network (SDS‐HXLP‐DNN), 39 respectively.…”

Section: Simulation Resultsmentioning

confidence: 99%

A hybrid HXPLS‐TMFCC parameterization and DCNN‐SFO clustering based speaker diarization system

Sailaja

Maloji

Mannepalli

2022

Concurrency and Computation

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The speaker diarization is considered to be the process by which the speaker signal is segmented, and the speaker identity is grouped into homogenous regions. The central point behind this scheme is the ability to distinguish between the speaker signal and each speaker signal with the label. As mass communication and meetings grow quickly, the diarization of the speakers is burden to improve the readability of the speech transcript. To solve this problem, tangent weighted mel‐frequency cepstral coefficient (TMFCC) and the extended linear prediction with autocorrelation snapshot feature extraction and the speaker diarization approach proposes a deep convolutional neural network (DCNN) for clustering and optimization using sailfish optimizer. A new development in the HXLPS extraction method is the holoentropy with extended linear prediction with autocorrelation snapshot. TMFCC makes more efficient and improves the effectiveness of the proposed scheme using lesser energy frame and higher energy framework. When achieve this, the voice activity detection method can recognize speech and non‐speech signals. Therefore, every segmented signal is represented by the d‐vector. The label of the speaker signal is clustered according to the speaker label used in the DCNN. The evaluation methods, like tracking distance, false alarm rate, diarization error rate examine the effectiveness.

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Section: Simulation Resultsmentioning

confidence: 99%

A hybrid HXPLS‐TMFCC parameterization and DCNN‐SFO clustering based speaker diarization system

Sailaja

Maloji

Mannepalli

2022

Concurrency and Computation

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show abstract

“…A Kahn process network was used to model applications in a heterogeneous multicore processor system, and the practical effectiveness of two heuristics, including genetic algorithms, in solving this class of design space exploration problems was experimentally evaluated [10]. With the optimization goal of reducing the overall execution time of applications on multicore processors, a combination of simulated annealing algorithms and pruning strategies is proposed to search for the optimal mapping between application to platform resources [11].…”

Section: Related Workmentioning

confidence: 99%

Construction of Dynamic Multiparallel Foreign Language Teaching Model Based on Multicore Processor

Huang¹,

Zheng²

2022

Mathematical Problems in Engineering

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Heterogeneous multicore processor systems, as one of the highlights of multicore processor systems, are widely loved by people for their high efficiency and low cost, and they have also become the most commonly used processor systems in embedded systems. In the process of research on heterogeneous multicore processor systems, system task scheduling is particularly important. A good task scheduling algorithm can give full play to system performance. In this paper, the intelligent approximation algorithm is applied to the task scheduling problem of heterogeneous multicore processor system, and the heterogeneous multicore processor system is obtained as a highlight in the multicore processor system. Relying on the characteristics of high efficiency and low cost, it is widely loved by people, and at the same time, it has become the most commonly used processor system in embedded systems. In the process of research on heterogeneous multicore processor systems, the system task scheduling problem is particularly important. A good task scheduling algorithm can give full play to system performance. Some commonly used heuristic task scheduling algorithms are insufficient in solving such problems. This paper combines the granularity-based wavefront parallel decoding algorithm and the fast fusion loop filter algorithm to apply pipeline parallel technology between pixels. Decoding reconstruction module and fast loop filter module realize the fusion of multilevel parallel decoding. Based on the multicore platform, a dynamic multiparallel scheduling algorithm is designed to realize two-way video real-time parallel high-speed decoding, which improves the core resource utilization rate and decoding execution efficiency of the multicore processing platform. This paper also designs an indicator collector, a read-write hit collector, and a cache block priority determiner to implement a dynamic generation strategy with low hardware overhead. Multimodal teaching has certain feasibility and effectiveness and can have a positive impact on the English reading motivation and English reading comprehension ability of university students. Multimodal teaching improves students’ English reading comprehension ability, deepens students’ understanding and memory of words, and broadens the scope of knowledge, which has a significant promoting effect. The research results show that multimodality can be applied to college English teaching, and it can achieve better results than traditional teaching methods. By comparing the test results before and after the test, there are obvious differences between the experimental class and the control class. Whether it is the paired sample T-test of the experimental class or the independent sample T-test of two teaching methods in two classes, it proves that the scores under the multimodal English teaching mode are higher than the traditional teaching mode. Multimodal classrooms provide students with many opportunities to participate in classroom activities and form a competitive learning atmosphere. This competitive learning atmosphere has become a driving force to promote student learning. Multimodal teaching methods help to cultivate students’ independent and cooperative learning. In a multimodal classroom, many activities require cooperation and discussion among students. When they encounter difficulties, they can help each other, discuss with each other, and cooperate to complete tasks. Therefore, the multimodal teaching method will stimulate students’ interest in learning, give full play to students’ initiative, and improve students’ English ability.

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A comprehensive review on applications of Raspberry Pi

Mathe,

Kondaveeti,

Vappangi

et al. 2024

Computer Science Review

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Real-Time Implementation of Speaker Diarization System on Raspberry PI3 Using TLBO Clustering Algorithm

Cited by 4 publications

References 45 publications

A hybrid HXPLS‐TMFCC parameterization and DCNN‐SFO clustering based speaker diarization system

A hybrid HXPLS‐TMFCC parameterization and DCNN‐SFO clustering based speaker diarization system

Construction of Dynamic Multiparallel Foreign Language Teaching Model Based on Multicore Processor

A comprehensive review on applications of Raspberry Pi

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