A Pitch Extraction Method with High Frequency Resolution for Singing Evaluation

2009 ◽  
Vol 129 (10) ◽  
pp. 1889-1901 ◽  
Author(s):  
Hideyo Takeuchi ◽  
Masahiro Hoguro ◽  
Taizo Umezaki
2021 ◽  
Vol 37 (1) ◽  
Author(s):  
Mai M. El Ghazaly ◽  
Mona I. Mourad ◽  
Nesrine H. Hamouda ◽  
Mohamed A. Talaat

Abstract Background Speech perception in cochlear implants (CI) is affected by frequency resolution, exposure time, and working memory. Frequency discrimination is especially difficult in CI. Working memory is important for speech and language development and is expected to contribute to the vast variability in CI speech reception and expression outcome. The aim of this study is to evaluate CI patients’ consonants discrimination that varies in voicing, manner, and place of articulation imparting differences in pitch, time, and intensity, and also to evaluate working memory status and its possible effect on consonant discrimination. Results Fifty-five CI patients were included in this study. Their aided thresholds were less than 40 dBHL. Consonant speech discrimination was assessed using Arabic consonant discrimination words. Working memory was assessed using Test of Memory and Learning-2 (TOMAL-2). Subjects were divided according to the onset of hearing loss into prelingual children and postlingual adults and teenagers. Consonant classes studied were fricatives, stops, nasals, and laterals. Performance on the high frequency CVC words was 64.23% ± 17.41 for prelinguals and 61.70% ± 14.47 for postlinguals. These scores were significantly lower than scores on phonetically balanced word list (PBWL) of 79.94% ± 12.69 for prelinguals and 80.80% ± 11.36 for postlinguals. The lowest scores were for the fricatives. Working memory scores were strongly and positively correlated with speech discrimination scores. Conclusions Consonant discrimination using high frequency weighted words can provide a realistic tool for assessment of CI speech perception. Working memory skills showed a strong positive relationship with speech discrimination abilities in CI.


2000 ◽  
Vol 66 (648) ◽  
pp. 2772-2777
Author(s):  
Yasuro HORI ◽  
Minoru SASAKI ◽  
Fumio FUJISAWA

2008 ◽  
Vol 55 (12) ◽  
pp. 4200-4209 ◽  
Author(s):  
A. Bellini ◽  
A. Yazidi ◽  
F. Filippetti ◽  
C. Rossi ◽  
G.-A. Capolino

2015 ◽  
Vol 713-715 ◽  
pp. 1031-1033
Author(s):  
Wei Jiang ◽  
Fang Yuan ◽  
Liu Qing Yang

This paper introduces the working principle and structure of direct digital frequency synthesizer. This paper select the technology of lookup table to design DDS because it has many advantages such as less consumption hardware resources, simple structure, output only small delay and so on. As a result, signal generator can produce many waveforms with good stability and high frequency resolution. Finally, test showed that the output wave of triangular signal frequency is greater than 1MHz and the highest sine wave frequency is 30MHz, the value of peak to peak is continuously adjustable in 50mV ~ 4V range. The result of study will provide theoretical guidance for the design of DDS.


Author(s):  
Pradeep Rengaswamy ◽  
M. Gurunath Reddy ◽  
K. Sreenivasa Rao ◽  
Pallab Dasgupta

1995 ◽  
Vol 35 (2) ◽  
pp. 98-112 ◽  
Author(s):  
Norman Salansky ◽  
Alexander Fedotchev ◽  
Alexander Bondar

2018 ◽  
Vol 12 (2) ◽  
pp. 73-84 ◽  
Author(s):  
Peng-Fei Wang ◽  
Xiao-Qing Luo ◽  
Xin-Yi Li ◽  
Zhan-Cheng Zhang

Stacked sparse autoencoder is an efficient unsupervised feature extraction method, which has excellent ability in representation of complex data. Besides, shift invariant shearlet transform is a state-of-the-art multiscale decomposition tool, which is superior to traditional tools in many aspects. Motivated by the advantages mentioned above, a novel stacked sparse autoencoder and shift invariant shearlet transform-based image fusion method is proposed. First, the source images are decomposed into low- and high-frequency subbands by shift invariant shearlet transform; second, a two-layer stacked sparse autoencoder is adopted as a feature extraction method to get deep and sparse representation of high-frequency subbands; third, a stacked sparse autoencoder feature-based choose-max fusion rule is proposed to fuse the high-frequency subband coefficients; then, a weighted average fusion rule is adopted to merge the low-frequency subband coefficients; finally, the fused image is obtained by inverse shift invariant shearlet transform. Experimental results show the proposed method is superior to the conventional methods both in terms of subjective and objective evaluations.


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