“…Fig. 21 is the framework diagram of pathological speech-based diagnosis designed by Wahengbam et al [ 183 ]. First, a deep pathological denoiser (DPD) block is obtained by training the silence and noise features using CNN and has an inverse STFT operation to revert the spectrum of the voice signal to the time domain.…”
Section: Pathological Voice Recognition For Diagnosis and Evaluationmentioning
confidence: 99%
“…Each subsystem uses multiple 3D convolutional network models for predictions. Finally, the fusion and decision-making are performed using the proposed group decision analogy strategy, and the accuracy was increased from 80.59% to 97.7% [ 183 ]. Fig.…”
Section: Pathological Voice Recognition For Diagnosis and Evaluationmentioning
confidence: 99%
“… Fig. 21 Graphical workflow of group decision analogy showing the multiclass pathology identification framework [ 183 ]. …”
Section: Pathological Voice Recognition For Diagnosis and Evaluationmentioning
confidence: 99%
“…As illustrated in Fig. 27 , improving the adaptability of speech processing-based smart equipment in different medical scenarios is a top priority because most current medical speech recognition systems are vulnerable to noise interference in practical application scenarios [ 19 , 20 , 183 ]. We can use a multi-channel microphone array and noise reduction algorithm to improve the quality of collected sound.…”
Section: Limitations and Future Directionsmentioning
“…Fig. 21 is the framework diagram of pathological speech-based diagnosis designed by Wahengbam et al [ 183 ]. First, a deep pathological denoiser (DPD) block is obtained by training the silence and noise features using CNN and has an inverse STFT operation to revert the spectrum of the voice signal to the time domain.…”
Section: Pathological Voice Recognition For Diagnosis and Evaluationmentioning
confidence: 99%
“…Each subsystem uses multiple 3D convolutional network models for predictions. Finally, the fusion and decision-making are performed using the proposed group decision analogy strategy, and the accuracy was increased from 80.59% to 97.7% [ 183 ]. Fig.…”
Section: Pathological Voice Recognition For Diagnosis and Evaluationmentioning
confidence: 99%
“… Fig. 21 Graphical workflow of group decision analogy showing the multiclass pathology identification framework [ 183 ]. …”
Section: Pathological Voice Recognition For Diagnosis and Evaluationmentioning
confidence: 99%
“…As illustrated in Fig. 27 , improving the adaptability of speech processing-based smart equipment in different medical scenarios is a top priority because most current medical speech recognition systems are vulnerable to noise interference in practical application scenarios [ 19 , 20 , 183 ]. We can use a multi-channel microphone array and noise reduction algorithm to improve the quality of collected sound.…”
Section: Limitations and Future Directionsmentioning
“…e parameter values may be arranged next in the resulting sound sequence or in the reverse order of the sound sequence. In the early 1950s, Wahengbam used a stochastic process to generate music fragments by hand [7]. By this time, computers had been created and were becoming a tool for music composition, and people began to use them to produce music.…”
We model in this paper the HPC cluster programming structure and perform a multidimensional computational analysis to design a vocal intelligent arrangement model based on HPC cluster programming using brilliant vocal arrangement. This paper proposes the construction scheme of the HPC interconnection simulation environment based on the full investigation of the research related to the construction of a supercomputer simulation environment at home and abroad. The rapid development of HPC technology has promoted the study of automatic music generation technology, which effectively avoids the traditional machine learning method of music. The rapid growth of HPC technology has facilitated the research of automated music generation techniques, which effectively avoids the overreliance of conventional machine learning methods on music rules and artificial design features. It achieves better results in complex music generation tasks. The key features of intelligent music arrangement are designed and implemented by summarizing the principles of converting paintings to music and then creating the rules of converting images to music and the corresponding relationships based on the theoretical basis. After the conversion rules are clarified, the specific design is carried out according to the computing process of the genetic algorithm. At the same time, the framework of the arrangement is combined with children’s aesthetic psychology; the evolutionary adaptation function is developed in combination with music theory, which finally ensures the motility of the generated music.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
