Fundamental Frequency Characteristics Research Articles

Feature Extraction and Musical Personality Analysis of Piano Artistic Performances

Abstract Starting from the basic process of piano music recognition, this paper focuses on the feature extraction process of piano art performance. In the feature extraction of piano art performance, the fundamental frequency characteristics of piano music signals were analyzed by using short-time Fourier transform theory, focusing on the feature extraction of musical pitch, time value, and tonality, and the spectrum analysis of piano music signals and the reconstruction of piano timbre were completed. The piano performance work, “Autumn Moon on the Peaceful Lake,” is used as an example to analyze the musical characteristics and personality. The overall structure of “Autumn Moon on the Peaceful Lake” is a typical three-part structure: the key is the bD palace seven-tone mode, the key is single, and the marking range of performance strength is ppp-pp-p-mf-mf-f-ff. In the three versions played by Yin Chengzong, Lang Lang, and Chen Jie, the average tempo of the performance was 31.5 beats, 26.5 beats, and 26.8 beats, respectively. The performance process of different pianists is a “slow-fast-slow” layout, and other passages are added to the individual free processing to further enhance the charm of the work.

Internal Short-Circuit Fault Location of Rail Transit Auxiliary Inverter Based on Voltage Deviation

In order to improve the ability of locating and detecting the internal short-circuit fault of rail transit auxiliary inverter, a method of locating the internal short-circuit fault of rail transit auxiliary inverter based on voltage deviation is proposed. The fault sensing information acquisition module of rail transit auxiliary inverter is constructed by using the detection method of fundamental frequency characteristics of differential mode current of excitation winding, and the voltage deviation analysis of rail transit auxiliary inverter under internal short-circuit fault state is realized by using the methods of MVCR excitation winding control and output reactive power adjustment. Combined with the dynamic distribution characteristics of output harmonics and initial phase of rail transit auxiliary inverter, the equivalent compensation and capacity expansion methods of excitation winding circuit are adopted. The signal test model of fault location is established, and the spectral characteristics of the internal short-circuit fault signal of rail transit auxiliary inverter are extracted. A fault joint feature location and monitoring model is established by using the data clustering analysis method of fusion difference, and the internal short-circuit fault location detection of inverter is realized according to the voltage deviation distribution characteristics of inverter in short-circuit state. The test results show that the accuracy of short-circuit fault location in rail transit auxiliary inverter is high, and the dynamic estimation ability of inverter output voltage deviation is strong.

Innovative Teaching of College Vocal Music Courses Aided by Information Integration Technology

Abstract The article adopts the correlation function method to extract the fundamental frequency features of vocal speech signals and processes this information through the cepstrum method. Then, the audio signals are corrected by linear smoothing to match the features of the students’ audio signals with the features of the musical score to determine the accuracy of the vocal music. The study also includes quantization, frame-splitting, and windowing preprocessing of the audio signals, and constructing an unwritten audio signal feature set based on correlation feature selection. The article also proposes an innovative metaphorical method for improving the teaching mode of vocal music courses in colleges and universities. It analyzes the auxiliary role of information technology in teaching vocal music courses by combining simulation experiments with the effects of practical application. The study results show that the students’ vocal singing fundamental frequency range is close to the standard, roughly 123 to 200, with a maximum error value of 24. With the assistance of information fusion technology, the average score increment of the students’ vocal singing reaches 6.088 points, proving this method’s effectiveness in improving the quality of verbal music teaching.

On the vibrations of sandwich structure with magneto-electro-rheological core and composite face sheet via mesh-free method

Multidirectional functionally graded materials due to high-mechanical resistance with other extraordinary properties in each direction have received much attention in recent years. Thus, in the current research, a numerical solution is presented to study the fundamental frequency characteristics of an MD-FG sector annular plate in a supersonic airflow. For obtaining exact displacement fields, the refined zig-zag theory is employed to obtain the transverse shear strain through layers of the composite lamina. A quasi-3D new refined theory is presented to model the displacement fields of the current sandwich structure. The Legendre-radial point interpolation method shape function is used to estimate the displacements while the mesh-free strong form approach discretizes the equations of motion and boundary conditions. Finally, the results show that Mach number, free stream speed, air yaw angle, temperature, and aerodynamic pressure have important roles in the dynamic stability performance of the sandwich sector annular plate in supersonic airflow. The results of the current report can be used in various high-temperature multidirectional compositionally graded materials for future works.

Developmental Changes in Acoustic Characteristics of Speech of Estonian Adolescents

The paper introduces the Estonian Adolescent Speech Corpus and explores the developmental changes in speech production based on acoustic characteristics of fundamental frequency (F0), formant frequencies, and speech tempo as a function of age and gender. Age- and gender-related anatomic changes in adolescence have implications for speech acoustics: a sudden drop of F0 at puberty in boys, and an almost gradual decrease of the acoustic vowels space. In parallel with anatomic changes, the development of the speech motor system is manifested as the increase of speaking rate. The analysis of fundamental frequency (F0) shows that in both male and female speakers, the F0 decreases gradually at the age from 9 to 12 years, then in males F0 drops ca by 100 Hz at the age of 12–15 due to puberty voice change, and becomes stable at the age of 15–18; in female speakers, a gradual decrease of F0 continues till the age 18. The formant frequencies of vowels decrease gradually from 10 to 15 years in both genders and the quality of vowels stabilizes at the age of 15–18 years, gender-specific differences emerge at the age of 12–13. Speech rate increases from 4 syllables per second in 9–10 years to 5.1 syllables per second in 14 years and becomes stable between the ages of 15 and 18, gender differences are not significant. The results of the current study can be considered as reference data that are typical for Estonian-speaking individuals aged 9–18 years with normal language development.

Measuring Prosodic Entrainment in Conversation: A Review and Comparison of Different Methods.

This study aims to further our understanding of prosodic entrainment and its different subtypes by analyzing a single corpus of conversations with 12 different methods and comparing the subsequent results. Entrainment on three fundamental frequency features was analyzed in a subset of recordings from the LUCID corpus (Baker & Hazan, 2011) using the following methods: global proximity, global convergence, local proximity, local convergence, local synchrony (Levitan & Hirschberg, 2011), prediction using linear mixed-effects models (Schweitzer & Lewandowski, 2013), geometric approach (Lehnert-LeHouillier, Terrazas, & Sandoval, 2020), time-aligned moving average (Kousidis et al., 2008), HYBRID method (De Looze et al., 2014), cross-recurrence quantification analysis (e.g., Fusaroli & Tylén, 2016), and windowed, lagged cross-correlation (Boker et al., 2002). We employed entrainment measures on a local timescale (i.e., on adjacent utterances), a global timescale (i.e., over larger time frames), and a time series-based timescale that is larger than adjacent utterances but smaller than entire conversations. We observed variance in results of different methods. Results suggest that each method may measure a slightly different type of entrainment. The complex implications this has for existing and future research are discussed.

Compact bowtie-shaped microstrip patch antennas with low cross-polarization and wideband harmonic suppression

A compact microstrip-fed bowtie-shaped circular ring slot antenna with a fundamental resonant frequency at 5.5 GHz for WLAN standards is initially studied. By introducing two bilateral triangles (BLT) slits symmetrically on either side of the radiator with a defected ground structure (DGS), wideband harmonic suppression (HMS) has been achieved. To get the dual-band i.e., lower band at 5.5 GHz and the upper band at 17.41 GHz for direct broadcast services (DBS) transmission satellite communication applications and additional suppress the harmonics excited by the insert-fed cut, a single inverted U-shaped slot (DGS) is integrated into the bowtie-shaped patch. Bowtie-shaped single-band patch (BSBP) antenna measured impedance bandwidth (IBW) from 5.218–6.225 GHz (17.6%) and insert-fed bowtie-shaped dual-band monopole (IBDBM) antenna measured IBW from 5.254–6.168 GHz (16.61%) for the lower band and from 17.213–17.856 GHz (3.66%) for the upper band, a wideband HMS up to 3.17 f0 (f0= fundamental resonant frequency) characteristics are achieved in both cases. The proposed antennas have a wideband HMS, compact size, and a significant reduction in cross-polarization levels in E-plane radiation patterns. The design parameters are also predicted using a machine learning regression technique built on an artificial neural network and convolutional neural network algorithm.

Histoire de la description de la parole: de l'introspection à l'instrumentation éd. par Christelle Dodane, et Claudia Schweitzer

Reviewed by: Histoire de la description de la parole: de l'introspection à l'instrumentation éd. par Christelle Dodane, et Claudia Schweitzer Wladyslaw Cichocki Dodane, Christelle, et Claudia Schweitzer, éd. Histoire de la description de la parole: de l'introspection à l'instrumentation. Champion, 2021. ISBN 978-2-7453-5595-9. Pp. 404. This collection of eleven papers (plus a preface and an afterword) deals with aspects of the historical development—from the sixteenth to the early twentieth centuries—of the description of speech as found in research on French. The main theme is that the notations and analytical techniques currently used in phonetic research were developed over a long period of time. Before the nineteenth century, knowledge about speech was limited to what could be perceived by auditory perception, physical observation, or intuition. However, major developments in speech research were to take place in the late nineteenth century with the invention of analytical instruments such as the kymograph, palatographic mirrors, and magnetic recordings. This theme is developed in each of the three sections of the book. The first section discusses the description of prosody. While sixteenth-century grammarians considered prosody to be an important part of rhetorical art, they tended to avoid it in their research because they had difficulty describing it. The discovery of instruments that brought into focus the acoustic features of fundamental frequency, intensity, and duration helped in the creation of notational systems that were sensitive to the organization of rhythm and intonation. The second section of the book presents the development of transcription systems for segmental sounds. Research began with two types of systems, "organic" notations based on physiological articulation and writing systems that were close to the Latin alphabet; these would eventually lead to the invention of the International Phonetic Alphabet. The third section of papers outlines the growth of experimental phonetics laboratories in Paris, Grenoble, Montpellier, and Strasbourg. It establishes connections between this development and research on déclamation, gesture, and singing. Among the topics covered are the legacies of Abbé Jean-Pierre Rousselot, the phonetician and dialectologist who was the founder of experimental phonetics, and Maurice Grammont, whose research on phonetic variation was a precursor to the field of sociophonetics. Many of the nineteen specialists who contributed papers to this volume are associated with the Praxiling Laboratoire de linguistique at Université Paul-Valéry Montpellier 3, and they represent different fields: phonetics, phonology, speech pathology, musicology, history of linguistics, and traditional grammar. This diversity may account for some of the differences in writing style and for the loose cohesion between chapters. A noticeable shortcoming is the small number of illustrations in the chapters that discuss the development of transcription systems. It is noteworthy that the volume is accompanied by an online booklet that provides links to videos and excerpts from rare manuscripts (including portraits, descriptive texts, and demonstrations of instruments like the kymograph). This book will be of particular [End Page 171] interest to phoneticians and advanced students of phonetics as well as to those who study the history of linguistics. More generally, anyone who carries out computer-based analysis of speech—using software such as Praat, for example—will find useful historical nuggets that inspire an appreciation of and reflection on the origins of current notational and instrumental practices. [End Page 172] Wladyslaw Cichocki University of New Brunswick (Canada) Copyright © 2023 American Association of Teachers of French

Innovative design and optimization of a two-dimensional deployable nine-grid planar antenna mechanism with a flat reflection surface

With the development of the aerospace industry, space missions are becoming more complicated and diversified, and there is a demand for antenna mechanisms with a larger physical aperture. In this paper, a planar deployable mechanism is proposed, which can form a flat reflection surface with a small gap between plates. To this end, a novel large-scale two-dimensional deployable nine-grid planar antenna mechanism is designed. First, two antenna folding schemes and four supporting mechanism schemes are proposed. Through comparison analysis, the antenna configuration scheme with the best comprehensive performance is selected. A kinematic model of the deployable mechanism is established, and its kinematic characteristics are analyzed. Then, the correctness of the kinematic model is verified by comparing the analytical and simulation results of the kinematic model. Subsequently, a finite element model of the antenna is developed. Based on the response surface method, the structural parameters of the support rods of the antenna are optimized, and a set of optimized solutions with lightweight and high fundamental frequency characteristics are obtained. Finally, a prototype of the proposed nine-grid planar antenna is fabricated. The feasibility of the deployment principle and the rationality of the designed mechanism are verified by deployment experiments.

Static and Eigenvalue Analysis of Electrostatically Coupled and Tunable Shallow Micro-Arches for Sensing-Based Applications.

This paper investigated the mechanical performance of an electrostatically tunable microbeams-based resonators. The resonator was designed based on two initially-curved microbeams that are electrostatically coupled, offering the potential for improved performance compared to single-beam based resonators. Analytical models and simulation tools were developed to optimize the resonator design dimensions and to predict its performance, including its fundamental frequency and motional characteristics. The results show that the electrostatically-coupled resonator exhibits multiple nonlinear phenomena including mode veering and snap-through motion. A coexistence of two stable branches of solutions for a straight beam case was even obtained due to the direct effect of the coupling electrostatic force with the other curved beam. Indeed, the results are promising for the better performance of coupled resonators compared to single-beam resonators and offer a platform for future MEMS applications including mode-localized based micro-sensors.

Echoing Retorts in Hárbarðsljóð and Lokasenna

Echoing Retorts in <i>Hárbarðsljóð</i> and <i>Lokasenna</i>

Multi-Dimensional Spectral Process for Cepstral Feature Engineering &amp; Formant Coding

The fundamental frequency feature is essential for Automatic Speech Recognition because its patterns convey a paralanguage and its tuning normalizes other speech features. Human speech is multidimensional because it is minimally represented by three variables: the intonation (or pitch), the formants (or timbre), and the speech resolution (or depth). These variables represent the hidden states of the local glottal variation, the vocal tract response, and the frequency scale, respectively. Computing them one by one is not as efficient as computing them together, so this article introduces a new speech feature extraction approach. The article is introductory; it focuses on the basic concepts of our new approach and does not elaborate on all applications. It demonstrates that the unit of a cepstral value, which is a spectral value of spectrums, is a unit of acceleration since its discrete variable, the quefrency, can be expressed in Hertz-per-microsecond. The article shows how to produce refined voice analysis from robust estimates and how to reconstruct speech signals from feature spaces. And it concludes that the pitch track of the new approach is as good as two open-source pitch extractors. Combining multiple processes, attenuating background noises, and enabling distant-speech recognition, we introduce the Speech Quefrency Transform (SQT) approach as well as multiple quefrency scales. SQT is a set of frequency transforms whose spectral leakages are controlled per a frequency-modulation model. SQT captures the stationarity of time series onto a hyperspace that resembles the cepstrogram when it is reduced for pitch track extraction.

Long-distance vocalizations of spotted hyenas contain individual, but not group, signatures.

In animal societies, identity signals are common, mediate interactions within groups, and allow individuals to discriminate group-mates from out-group competitors. However, individual recognition becomes increasingly challenging as group size increases and as signals must be transmitted over greater distances. Group vocal signatures may evolve when successful in-group/out-group distinctions are at the crux of fitness-relevant decisions, but group signatures alone are insufficient when differentiated within-group relationships are important for decision-making. Spotted hyenas are social carnivores that live in stable clans of less than 125 individuals composed of multiple unrelated matrilines. Clan members cooperate to defend resources and communal territories from neighbouring clans and other mega carnivores; this collective defence is mediated by long-range (up to 5 km range) recruitment vocalizations, called whoops. Here, we use machine learning to determine that spotted hyena whoops contain individual but not group signatures, and that fundamental frequency features which propagate well are critical for individual discrimination. For effective clan-level cooperation, hyenas face the cognitive challenge of remembering and recognizing individual voices at long range. We show that serial redundancy in whoop bouts increases individual classification accuracy and thus extended call bouts used by hyenas probably evolved to overcome the challenges of communicating individual identity at long distance.

Isolated Word-Based Spoken Dialogue System Using Odia Phones

Aims: To develop Spoken Dialogue System in Indian language with Voice response and voice based biometric feature. Backgrounds: Most of research works in spoken dialogue system are carried out in U.S. and Europe and currently, few government funding projects on spoken dialogue system (SDS) are carried out in Indian academic institutes. Objective: We have tried to use our developed spoken language system to eliminate the desktop clutter. It is very normal tendency of computer users to place the most frequently used files, folders, applications shortcuts on their computer’s desktop. Cluttering of desktop not only slows down the productivity of computer but also looks very messy and very difficult to find files as well. Therefore, we tried to use the spoken dialogue system to eliminate the desktop clutters in painless manner and the services are provided to the computer users by opening the files, folders and frequently used application of users in spoken command mode with voice response. Methods: In this research article, we have attempted to utilize an Indian spoken language for communication with spoken dialogue system. We have adopted a statistical machine learning algorithm called Hidden Markov Model for development of speech recognition engine. The speaker verification module is developed using fuzzy c-means algorithm. Speech synthesis is carried out using diphone corpus. Results: The speaker verification module has yielded satisfactory results with average accuracy of 66.2% using FCM algorithm. It is also seen that fundamental frequency and formant frequency carry the distinctive characteristics of speaker verification over Indian spoken language. The vital module of SDS i.e. speech recognition engine is developed by using HMM, a statistical algorithm. It is observed that word accuracy of ASR engine is 78.22 % and 62.31 % for seen and unseen users respectively. The voice response is given to the user in terms of synthesized speech. The audio quality of synthesized speech is measured using the MOS test. The MOS test value is found as 3.8 and 3.6 over two distinct groups of listeners. Conclusion: In this research paper, we have developed a spoken dialogue system based on Odia language phone set. We have integrated speaker verification module in order to provide additional biometric based security.

Fundamental frequency feature warping for frequency normalization and data augmentation in child automatic speech recognition

Effective child automatic speech recognition (ASR) systems have become increasingly important due to the growing use of interactive technology. Due to the lack of publicly available child speech databases, young child ASR systems often rely on older child or adult speech for training data. However, there is a large acoustic mismatch between child and adult speech. This study proposes a novel fundamental frequency (fo)-based frequency warping technique for both frequency normalization and data augmentation to combat this acoustic mismatch and address the lack of available child speech training data. The technique is inspired by the tonotopic distances between formants and fo, developed to model human vowel perception. The tonotopic distances are reformulated as a linear relationship between fo and vowel formants on the Mel scale. This reformulation is verified using fo and formant measurements from child utterances. The relationship is further generalized such that the frequency warping technique only relies on two parameters. The LibriSpeech ASR corpus is used for training, and both the OGI Kids’ Speech and CMU Kids Corpora are used for both training and testing. A single word ASR experiment and a continuous read speech ASR experiment are performed to evaluate the fo-based frequency normalization and data augmentation techniques. In the single word experiment, the system using fo-based frequency normalization significantly improved over the baseline system with no normalization, with a relative improvement of up to 22.3%, when the mismatch between training and testing data was large. In the continuous speech experiment, the combination of fo-based frequency normalization and data augmentation resulted in a relative improvement of 19.3% over the baseline. Additionally, in all experiments, the fo-based techniques outperformed other techniques such as vocal tract length normalization (VTLN) or vocal tract length perturbation (VTLP). Results were validated using Gaussian mixture model (GMM), deep neural network (DNN), and bidirectional long–short term memory (BLSTM) acoustic models.

Optimization the Accuracy of FFNN Based Speaker Recognition System Using PSO Algorithm

Speaker recognition systems use a model that learns a speaker's speech by inputting an audio recording and processing it. Time-varying signal, with frequencies that continuously change, is identified as a speech signal. There are many uncertain attributes to speech; thus traditional speech recognition techniques such as using zero crossings and the Fourier Transform are not up to the task. It aims to be accomplished with the aim of helping two causes. The first part is designed to address speaker identification technology that is resistant to noise. While most prior solutions have relied on changing mel frequency cepstrum coefficients, with a Fundamental frequency feature coefficient, this proposal integrates both of these modifications with a new cepstrum component. In order to construct the feature matrix, the system is fed with two-hundred and fifty speech imprints that are used to apply features extraction techniques. The matrix is used to teach the algorithm about features, and each one is then evaluated using incomplete data (thirty percent of total data in features matrix). Speaker recognition models with improved accuracy are developed by studying the algorithms invasively. These variables (metrics) are generated for each algorithm and applied to the algorithm for recognition accuracy and the time required to achieve that accuracy. When tested against previous research, the findings show that the Feed Forward Neural Network-based Particle Swarm Optimization method has been better. This model can accurately identify 96% of the input with less processing time. According to the findings, optimization utilizing advanced particle swarm optimization (a.k.a. Particle Swarm Optimization) is most likely responsible for the higher accuracy seen in speaker identification.

When Music Speaks: An Acoustic Study of the Speech Surrogacy of the Nigerian Dùndún Talking Drum

Yorùbá dùndún drumming is an oral tradition which allows for manipulation of gliding pitch contours in ways that correspond to the differentiation of the Yorùbá linguistic tone levels. This feature enables the drum to be employed as both a musical instrument and a speech surrogate. In this study, we examined four modes of the dùndún talking drum, compared them to vocal singing and talking in the Yorùbá language, and analyzed the extent of microstructural overlap between these categories, making this study one of the first to examine the vocal surrogacy of the drum in song. We compared the fundamental frequency, timing pattern, and intensity contour of syllables from the same sample phrase recorded in the various communicative forms and we correlated each vocalization style with each of the corresponding drumming modes. We analyzed 30 spoken and sung verbal utterances and their corresponding drum and song excerpts collected from three native Yorùbá speakers and three professional dùndún drummers in Nigeria. The findings confirm that the dùndún can very accurately mimic microstructural acoustic temporal, fundamental frequency, and intensity characteristics of Yorùbá vocalization when doing so directly, and that this acoustic match systematically decreases for the drumming modes in which more musical context is specified. Our findings acoustically verify the distinction between four drumming mode categories and confirm their acoustical match to corresponding verbal modes. Understanding how musical and speech aspects interconnect in the dùndún talking drum clarifies acoustical properties that overlap between vocal utterances (speech and song) and corresponding imitations on the drum and verifies the potential functionality of speech surrogacy communications systems.

Research on sub/super‐synchronous oscillation suppression method for direct‐drive wind turbine based on energy compensation

A method to suppress sub/super-synchronous oscillation of direct-drive wind turbines based on energy compensation is proposed. First, the transient energy model of direct-drive wind turbine is built, and the negative damping energy terms that lead the oscillation of direct-drive wind turbine are extracted. On this basis, the expressions of voltage compensation terms corresponding to negative damping energy terms are derived by backward deduction, thus supplementary energy branches are constructed, and their impact on fundamental frequency characteristics of wind turbine is analyzed. And then, with the compensation energy of supplementary branches reaching the maximum and the increment of fundamental-frequency voltage being the minimum as the objective, and with the frequency-domain characteristic and fundamental-frequency voltage characteristic of control links being satisfied as constraints, a scheme to optimize the compensation coefficients of multiple branches is established. Finally, the model of direct-drive wind turbine is built in RT-LAB for hardware-in-the-loop tests. Simulation results verify that, the method can realize fast frequency-dependent suppression of sub/super-synchronous oscillation in different frequency bands concerning different grid strengths.

Speech emotion recognition based on SVM and KNN classifications fusion

Recognizing the sense of speech is one of the most active research topics in speech processing and in human-computer interaction programs. Despite a wide range of studies in this scope, there is still a long gap among the natural feelings of humans and the perception of the computer. In general, a sensory recognition system from speech can be divided into three main sections: attribute extraction, feature selection, and classification. In this paper, features of fundamental frequency (FEZ) (F0), energy (E), zero-crossing rate (ZCR), fourier parameter (FP), and various combinations of them are extracted from the data vector, Then, the principal component analysis (PCA) algorithm is used to reduce the number of features. To evaluate the system performance. The fusion of each emotional state will be performed later using support vector machine (SVM), K-nearest neighbor (KNN), In terms of comparison, similar experiments have been performed on the emotional speech of the German language, English language, and significant results were obtained by these comparisons.

Distinctive Features of a Threatening Demand and Entreaty at the Fundamental Frequency Level

the article gives the results of an experimental and phonetic study on the prosodic differentiation at the level of the fundamental frequency of two illocutionary acts – a threatening demand and entreaty (on the material of German). It is known that prosody affects a communicative process, and depending on the nature of a prosodic structure, the meaning of a statement changes. The same lexical and grammatical corpus can act as a threat, demand, entreaty, sarcastic statement, etc. The distinctive features of these speech acts are directly influenced by the perceived prosodic features of fundamental frequency, intensity and duration. On the basis of auditory, acoustic, and mathematical and statistical analyzes, the distinctive features of the two illocutionary acts were identified at the level of the fundamental frequency. The study shows that the two illocutionary acts differ at the level of the fundamental frequency, despite the fact that they belong to the same speech act – directive. The results obtained can be used in research in the field of phonology, experimental and applied phonetics. In addition, the obtained material may be of interest to forensic experts when conducting a phonoscopic examination