scholarly journals Acoustic evolution of old Italian violins from Amati to Stradivari

2018 ◽  
Vol 115 (23) ◽  
pp. 5926-5931 ◽  
Author(s):  
Hwan-Ching Tai ◽  
Yen-Ping Shen ◽  
Jer-Horng Lin ◽  
Dai-Ting Chung
Keyword(s):  
Central Region ◽  
Vocal Tract ◽  
Predictive Coding ◽  
Linear Predictive Coding ◽  
Response Curves ◽  
Male And Female ◽  
Human Voice ◽  
Construction Methods ◽  
Acoustic Output ◽  
The Ideal

The shape and design of the modern violin are largely influenced by two makers from Cremona, Italy: The instrument was invented by Andrea Amati and then improved by Antonio Stradivari. Although the construction methods of Amati and Stradivari have been carefully examined, the underlying acoustic qualities which contribute to their popularity are little understood. According to Geminiani, a Baroque violinist, the ideal violin tone should “rival the most perfect human voice.” To investigate whether Amati and Stradivari violins produce voice-like features, we recorded the scales of 15 antique Italian violins as well as male and female singers. The frequency response curves are similar between the Andrea Amati violin and human singers, up to ∼4.2 kHz. By linear predictive coding analyses, the first two formants of the Amati exhibit vowel-like qualities (F1/F2 = 503/1,583 Hz), mapping to the central region on the vowel diagram. Its third and fourth formants (F3/F4 = 2,602/3,731 Hz) resemble those produced by male singers. Using F1 to F4 values to estimate the corresponding vocal tract length, we observed that antique Italian violins generally resemble basses/baritones, but Stradivari violins are closer to tenors/altos. Furthermore, the vowel qualities of Stradivari violins show reduced backness and height. The unique formant properties displayed by Stradivari violins may represent the acoustic correlate of their distinctive brilliance perceived by musicians. Our data demonstrate that the pioneering designs of Cremonese violins exhibit voice-like qualities in their acoustic output.

Speech to Text Processing for Interactive Agent of Virtual Tour Navigation

2019 ◽  
Vol 1 (1) ◽  
pp. 31
Author(s):  
Dian Ahkam Sani ◽  
Muchammad Saifulloh
Keyword(s):  
Speech Recognition ◽  
Text Processing ◽  
Predictive Coding ◽  
Digital Signal ◽  
Recognition System ◽  
Human Interaction ◽  
Linear Predictive Coding ◽  
Voice Input ◽  
Human Voice ◽  
Backpropagation Method

The development of science and technology is one way to replace the method of human interaction with computers, one of which is to provide voice input. Conversion of sound into text form with the Backpropagation method can be understood and realized through feature extraction, including the use of Linear Predictive Coding (LPC). Linear Predictive Coding is one way to represent the signal in obtaining the features of each sound pattern. In brief, the way this speech recognition system worked was by inputting human voice through a microphone (analog signal) which then sampled with a sampling speed of 8000 Hz so that it became a digital signal with the assistance of sound card on the computer. The digital signal from the sample then entered the initial process using LPC, so that several LPC coefficients were obtained. The LPC outputs were then trained using the Backpropagation learning method. The results of the learning were classified with a word and stored in a database afterwards. The results of the test were in the form of an introduction program that able display the voice plots. the results of speech recognition with voice recognition percentage of respondents in the database iss 80% of the 100 data in the test in Real Time

scholarly journals Variation of Formant Frequency with Nepalese Vowels

2020 ◽  
pp. 16-21
Author(s):  
S.K. Adhikari
Keyword(s):  
Vocal Tract ◽  
Free Field ◽  
Predictive Coding ◽  
Initial Position ◽  
Frequency Range ◽  
Linear Predictive Coding ◽  
Formant Frequency ◽  
Formant Frequencies ◽  
Speech Spectrum ◽  
Shape And Size

The regions of speech spectrum in which the frequency corresponds to relatively large amplitude are known as formants. For any vocalic sounds, number of formants may occur in the frequency range 0 to 4000 Hz. The formant frequencies of speech sounds are directly depending up on the shape and size of vocal tract. The aim of study was to study the variation of formant frequency with Nepalese vowels. Ten Nepalese vowels word in initial position /VC/ as spoken three times by 10 male and 10 female Nepali speakers were recorded in system in the free field of partially acoustically treated room. PRRAT software is used to digitize and analyze the data. Linear predictive coding (LPC) spectra were obtained for each of vowels and formant frequencies were measured. By plotting curve between formant frequencies and vowels, explain their variation.  

Epenthetic Vowels and Phonemic Vowels: Same or Different?

2020 ◽  
Vol 5 (5) ◽  
pp. 1339-1346
Author(s):  
Christina Akbari ◽  
Katsura Aoyama
Keyword(s):  
Second Language ◽  
Acoustic Signal ◽  
Vocal Tract ◽  
Predictive Coding ◽  
Linear Predictive Coding ◽  
Acoustic Characteristics ◽  
Second Language Speakers ◽  
The Mean ◽  
Double Cluster ◽  
Target Words

Purpose This study was designed to further investigate epenthetic vowels produced by Persian second language speakers of English. Specifically, the purpose was to compare epenthetic and phonemic vowels to determine if acoustic differences existed or if the epenthetic vowels were quantitative “copies” of their phonemic counterparts. Method Twenty Persian speakers each produced 120 target words. The target words were composed of two different double cluster compositions (obstruent + glide and obstruent + liquid) as well as obstruent + liquid triple clusters and obstruent + glide triple cluster combinations. The target words occurred in a phonetic environment that was either preceded by a consonant /t/ or occurred in isolation. This resulted in 2400 tokens. The tokens underwent Linear Predictive Coding to determine the F1 and F2 formant measurements as well as the durations of the epenthetic and phonemic vowels. Formants are the resonance of the vocal tract. F1 is the lowest-frequency formant while F2 is the next highest ( Kent & Read, 2002 ). Linear Predictive Coding allows for the acoustic signal to be represented spectrally for analysis. Results A total of 236 epenthetic voamp'wels and their phonemic counterparts were acoustically analyzed. The phonemic vowels were found to be significantly longer than the epenthetic vowels. The epenthetic vowels were also found to have significantly lower F1 values. As a group, the mean F2 values were not significantly different from the F2 values of the phonemic vowels. However, significant differences in F2 values were found when specific vowel comparisons were made. Conclusions The data indicate that prothetic epenthetic vowels are not copies of the phonemic vowels that they precede. They differ quantitatively in terms of durations, F1, and F2 values. The findings of this study coincide with the findings of other researchers concerning the acoustic characteristics of anaptyctic epenthetic vowels. These results indicate similarities between prothetic and anaptyctic epenthetic vowels.

Formant Frequency Fluctuation as an Index of Motor Steadiness in the Vocal Tract

1983 ◽  
Vol 26 (2) ◽  
pp. 297-304 ◽  
Author(s):  
Bruce R. Gerratt
Keyword(s):  
Vocal Tract ◽  
Involuntary Movement ◽  
Predictive Coding ◽  
Normal Subjects ◽  
Acoustic Method ◽  
Frequency Fluctuation ◽  
Linear Predictive Coding ◽  
Vowel Production ◽  
Formant Frequency ◽  
Second Formant

Involuntary movement of the articulatory structures can interfere with the accurate placement of the articulators during consonant production and may also result in distortion of vowel quality. An acoustic method was used to assess motor steadiness in the vocal tract musculature superior to the glottis during vowel production by five subjects with abnormal involuntary orofacial movements associated with tardive dyskinesia and 10 normal subjects. A linear predictive coding technique of spectral analysis yielded formant frequencies from the sustained productions of//. Based on the premise that changes in vocal tract configuration can be measured as changes in formant frequency, the sequential segment-to-segment fluctuations of the second formant frequency of these vowel samples were computed and used as an index of motor steadiness. Results showed that formant frequency fluctuation measures for four of the five tardive dyskinetic patients were substantially larger than those of the normal subjects, indicating a reduction of motor steadiness in these four subjects. Factors influencing the validity of this procedure and implications for its use are discussed.

scholarly journals Optimizing Vowel Formant Measurements in Four Acoustic Analysis Systems for Diverse Speaker Groups

2016 ◽  
Vol 25 (3) ◽  
pp. 335-354 ◽  
Author(s):  
Ekaterini Derdemezis ◽  
Houri K. Vorperian ◽  
Ray D. Kent ◽  
Marios Fourakis ◽  
Emily L. Reinicke ◽  
...  
Keyword(s):  
Acoustic Analysis ◽  
Predictive Coding ◽  
Linear Predictive Coding ◽  
Formant Frequency ◽  
Male And Female ◽  
Consensus Analysis ◽  
Optimal Analysis ◽  
Software Packages ◽  
Vowel Formant ◽  
Female Children

Purpose This study systematically assessed the effects of select linear predictive coding (LPC) analysis parameter manipulations on vowel formant measurements for diverse speaker groups using 4 trademarked Speech Acoustic Analysis Software Packages (SAASPs): CSL, Praat, TF32, and WaveSurfer. Method Productions of 4 words containing the corner vowels were recorded from 4 speaker groups with typical development (male and female adults and male and female children) and 4 speaker groups with Down syndrome (male and female adults and male and female children). Formant frequencies were determined from manual measurements using a consensus analysis procedure to establish formant reference values, and from the 4 SAASPs (using both the default analysis parameters and with adjustments or manipulations to select parameters). Smaller differences between values obtained from the SAASPs and the consensus analysis implied more optimal analysis parameter settings. Results Manipulations of default analysis parameters in CSL, Praat, and TF32 yielded more accurate formant measurements, though the benefit was not uniform across speaker groups and formants. In WaveSurfer, manipulations did not improve formant measurements. Conclusions The effects of analysis parameter manipulations on accuracy of formant-frequency measurements varied by SAASP, speaker group, and formant. The information from this study helps to guide clinical and research applications of SAASPs.

scholarly journals Real-Time Vocal Tract Modelling

2007 ◽  
Vol 2007 ◽  
pp. 1-8
Author(s):  
A. Benkrid ◽  
A. Benallal ◽  
K. Benkrid
Keyword(s):  
Transmission Line ◽  
Wave Scattering ◽  
Speech Synthesis ◽  
Vocal Tract ◽  
Predictive Coding ◽  
Realistic Model ◽  
Three Dimensions ◽  
Linear Predictive Coding ◽  
One Dimensional ◽  
Established Technique

To date, most speech synthesis techniques have relied upon the representation of the vocal tract by some form of filter, a typical example being linear predictive coding (LPC). This paper describes the development of a physiologically realistic model of the vocal tract using the well-established technique of transmission line modelling (TLM). This technique is based on the principle of wave scattering at transmission line segment boundaries and may be used in one, two, or three dimensions. This work uses this technique to model the vocal tract using a one-dimensional transmission line. A six-port scattering node is applied in the region separating the pharyngeal, oral, and the nasal parts of the vocal tract.

scholarly journals Speech Recognition System for Isolated Tamil Words using Random Forest Algorithm

2020 ◽  
Vol 9 (1) ◽  
pp. 2431-2435
Keyword(s):  
Feature Extraction ◽  
Speech Recognition ◽  
Random Forest ◽  
Vocal Tract ◽  
Predictive Coding ◽  
Recognition System ◽  
Linear Predictive Coding ◽  
Mel Frequency Cepstral Coefficients ◽  
Speech Corpus ◽  
Training Time

ASR is the use of system software and hardware based techniques to identify and process human voice. In this research, Tamil words are analyzed, segmented as syllables, followed by feature extraction and recognition. Syllables are segmented using short term energy and segmentation is done in order to minimize the corpus size. The algorithm for syllable segmentation works by performing the STE function of the continuous speech signal. The proposed approach for speech recognition uses the combination of Mel-Frequency Cepstral Coefficients (MFCC) and Linear Predictive Coding (LPC). MFCC features are used to extract a feature vector containing all information about the linguistic message. The LPC affords a robust, dependable and correct technique for estimating the parameters that signify the vocal tract system.LPC features can reduce the bit rate of speech (i.e reducing the measurement of transmitting signal).The combined feature extraction technique will minimize the size of transmitting signal. Then the proposed FE algorithm is evaluated on the speech corpus using the Random forest approach. Random forest is an effective algorithm which can build a reliable training model as its training time is less because the classifier works on the subset of features alone.

Comparative assessment of average clinical smile parameters in male and female patients with orthognathic occlusion

2020 ◽  
Vol 25 (2) ◽  
pp. 121-126
Author(s):  
V. G. Galonsky ◽  
N. V. Tarasova ◽  
V. V. Aliamovskii ◽  
I. S. Leonovich
Keyword(s):  
Sexual Dimorphism ◽  
Male And Female ◽  
Young Males ◽  
Young Females ◽  
Female Patients ◽  
Evaluation Of Parameters ◽  
Distinguishing Features ◽  
Vertical Size ◽  
Relative Concept ◽  
The Ideal

Relevance. Separate issues in anthropomorphic sizes of relative norm of the ideal smile, its qualitative and qualitative parameters have not been addressed to sufficiently and are not properly reflected in scientific literature.Purpose. To determine distinguishing features in average smile parameters of the smile in male and female patients with orthognathic occlusion.Materials and methods. A clinical and anthropometric evaluation of parameters in main smile types was carried out for 150 young males and 150 young females aged 19-24 who had identical physiological development parameters.Results. It has been revealed that occurrence frequency of main smile types in patients with orthognathic occlusion has pronounced signs of sexual dimorphism which in over one half of the cases lies in predominance of the incisal smile type in males (52.7%) and the fascial type in females (55.3%). Occurence frequency of the cervical smile type totaled 25% among the studied patients of both genders. Average vertical size parameters in the incisal smile lies within the diapason of 3.91-4.91mm with surpassing by 1mm in males. Analogical data for the fascial smile type form the diapason of 6.21-6.73mm with surpassing by 0.52mm in females. The cervical smile type is characterised by larger vertical size forming the diapason of 7.94-8.91mm with surpassing by 0.97mm in males.Conclusion. The results of the study have shown that the “beautiful and ideal smile” is a relative concept having varied anthropometric characteristics and pronounced signs of sexual dimorphism lying in a broad spectrum of the dentofacial system norm notion with specific vectors for individual morphological deviations.

Exploring vowel formant estimation through simulation-based techniques

2020 ◽  
Vol 6 (s1) ◽  
Author(s):  
Tyler Kendall ◽  
Charlotte Vaughn
Keyword(s):  
Predictive Coding ◽  
Linear Predictive Coding ◽  
Fine Grained ◽  
Vowel Formant ◽  
Simulation Based ◽  
Insight Into

AbstractThis paper contributes insight into the sources of variability in vowel formant estimation, a major analytic activity in sociophonetics, by reviewing the outcomes of two simulations that manipulated the settings used for linear predictive coding (LPC)-based vowel formant estimation. Simulation 1 explores the range of frequency differences obtained when minor adjustments are made to LPC settings, and measurement timepoints around the settings used by trained analysts, in order to determine the range of variability that should be expected in sociophonetic vowel studies. Simulation 2 examines the variability that emerges when LPC settings are varied combinatorially around constant default settings, rather than settings set by trained analysts. The impacts of different LPC settings are discussed as a way of demonstrating the inherent properties of LPC-based formant estimation. This work suggests that differences more fine-grained than about 10 Hz in F1 and 15–20 Hz in F2 are within the range of LPC-based formant estimation variability.

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