Word recognition testing may be defined as a procedure to assess a listener’s ability to identify one-syllable words (such as phonetically-balanced/PB words) that are presented at a given suprathreshold level to arri...
Word recognition testing may be defined as a procedure to assess a listener’s ability to identify one-syllable words (such as phonetically-balanced/PB words) that are presented at a given suprathreshold level to arrive at a word recognition score. For Thai, Thammasat University and Ramathibodi Hospital Phonetically Balanced Word Lists 2015 (TU-RAMA PB’15) were created with five lists, each with 25 monosyllabic words. Besides its phoneme distributions being based on large-scale Thai spoken corpora, TU-RAMA PB’15 is in line with TU PB’14 with emphasis on phonetic balance, symmetrical phoneme occurrence, and word familiarity. To evaluate its homogeneity in terms of decibel intelligibility, the lists were recorded and presented to 10 normal hearing participants, ranging from 0 to 50 dB HL in 2 dB increments (ascending order) until they repeated correct verbal responses. Using logistic regression, regression slopes and intercepts were calculated to estimate percentage of correct performance at any given intensity and to construct psychometric functions for every list. Derived psychometric function slopes ranged from 0.2015 to 0.2262 while intensities required for 50% intelligibility ranged from 17.0876 to 20.8856. Two-way Chi-Square analysis performed on both parameters indicated that there was no significant difference among the five lists.
Vibrato is an important music performance technique for both voice and various music instruments. In this paper, a signal processing framework for vibrato analysis, manipulation and resynthesis is presented. In the an...
Vibrato is an important music performance technique for both voice and various music instruments. In this paper, a signal processing framework for vibrato analysis, manipulation and resynthesis is presented. In the analysis part, music vibrato is treated as a generalized descriptor of music timbre and the signal magnitude and instantaneous frequency are implemented as temporal features. Specifically, the magnitude track shows the dynamic variations of audio loudness, and the frequency track shows the frequency deviations varying with time. In the manipulation part, several manipulation methods for the magnitude track and the frequency track are implemented. The tracking results are manipulated in both the time- and the frequency-domain. These manipulation methods are implemented as an interactive process to allow musicians to manually adjust the processing parameters. In the resynthesis part, the simulated vibrato audio is created using sinusoidal resynthesis process. The resynthesis part serves three purposes: to imitate human music performance, to migrate sonic features across music performances, and to serve as creative audio design tools, e.g., to create non-existing vibrato characteristics. The source audio from human music performance and the resynthesized audio are compared using subjective listening tests to validate our proposed framework.
An adaptive MIMO detection algorithm for LTE-A system which is based on sphere detection is proposed in this paper. The proposed algorithm uses M-algorithm for reference to remove unreliable constellation candidates b...
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An adaptive MIMO detection algorithm for LTE-A system which is based on sphere detection is proposed in this paper. The proposed algorithm uses M-algorithm for reference to remove unreliable constellation candidates before search, and the number of constellation reservation is adaptively adjusted according to SNR. Simulations of LTE-A downlink show that the BER performance of the proposed detection algorithm is nearly the same as maximum likelihood (ML) detection algorithm. However, the complexity is reduced by about 30% compared with full constellation sphere detection.
This article was originally published online on 3 July 2014 without up-to-date affiliations. The affiliations are correct as they appear above. All online versi
This article was originally published online on 3 July 2014 without up-to-date affiliations. The affiliations are correct as they appear above. All online versi
This article was originally published online on 22 October 2012 with a misspelling in Mohammed AlShareef's name. The name is correct as it appears above. All on
This article was originally published online on 22 October 2012 with a misspelling in Mohammed AlShareef's name. The name is correct as it appears above. All on
Our recent publication1 contained an error in the list of references consisting of the omission on the following two references regarding the Composition Graded
Our recent publication1 contained an error in the list of references consisting of the omission on the following two references regarding the Composition Graded
There is an error in the GeOI substrate description. The thickness of SiO2 layer is 100 nm, instead of 1 μm.1 The labels for SiO2 and Ge layers are in the wrong
There is an error in the GeOI substrate description. The thickness of SiO2 layer is 100 nm, instead of 1 μm.1 The labels for SiO2 and Ge layers are in the wrong
The two legends in Figure 1, “type I” and “type II,” are reversed in the originally published version.1 The corrected version is showed in Fig. 1 herein.
The two legends in Figure 1, “type I” and “type II,” are reversed in the originally published version.1 The corrected version is showed in Fig. 1 herein.
The article was published online on February 27, 2012 with the acknowledgment missing. It was purely an unintentional error for which the authors regret.
The article was published online on February 27, 2012 with the acknowledgment missing. It was purely an unintentional error for which the authors regret.
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