Reviewerly/exHarmony huggingface dataset:
Comparison of vocal vibrato across multiple singers and vowels

Vibrato is a common technique used by singers to add expressiveness to their voice, which involves oscillating slightly above and below the target pitch. Analyzing the features of vibrato could be useful in projects such as identifying singers based on their voice or creating a more realistic autotune. The parameters of the vibrato explored included frequency modulation width, frequency modulation rate, and amplitude modulation width. To collect data on these parameters, various singers of different voice parts were recorded singing scales on different vowels with and without vibrato. The singers were recorded in an anechoic chamber to minimize the impact of the room on the samples. The scales were then cut down into individual pitches that could be analyzed. Using MATLAB, the vibrato parameters of the voice samples were calculated and graphed. The data was then compared between vowels sung by the same singer, and between different singers.
Archtop guitar dynamics—<i>Continuing research of archtop guitar acoustics</i>

The contemporary archtop guitar, designed by Lloyd Loar of the Gibson Mandolin-Guitar Mfg. Co. Ltd., will be 100 years old in 2023. Since the first L5 shipped in 1923, the archtop guitar has taken on a life of its own, having been reinvented by luthiers worldwide. Research of the archtop guitar is a new field in instrument acoustics. Measurement techniques developed by the bowed instrument research community have been adapted for guitar to offer a glimpse into the acoustic behavior of a century old, yet timeless guitar design. This talk will focus on the ongoing research that has been conducted since the 2021 publication of Archtop Guitar Dynamics by the Violin Society of America Papers. The archtop guitar shares features of violin family instruments, including a carved top, ff-hole perforations, and raised bridge. Beyond aesthetics, the archtop shares acoustic similarities to both violin and guitar family instruments resulting in a unique acoustic signature. Continuing research includes analysis of historically significant instruments from Gibson Mandolin-Guitar Mfg. Co. Ltd and the works of New York luthier, John D'Angelico as part of the Archtop Project: New York sponsored by D'Addario &amp; Co. and the Archtop Foundation.
The six prototype microphone elements that led to the Shure Unidyne I Model 55

Benjamin B. Bauer (1913–1979) was an ASA Fellow and awarded the ASA Silver Medal (1978). He held over 100 patents for acoustical/audio technology, with his 1941 patent being arguably the most significant: the Uniphase Acoustical Network principle, integral to the Shure Unidyne model 55 microphone. Introduced in 1939 and still manufactured today, the Shure Unidyne was the first moving coil unidirectional microphone using a single dynamic element. Six prototype mic elements for the Unidyne, fabricated by Bauer in the late 1930s, reside in the Shure archive. This presentation provides details on each prototype using photos, drawings, and Bauer’s text from his lab notebooks, providing insight into the creation of the one of the world’s most famous microphones.
Real-time processing approaches for interactive room acoustic auralization in the rtSOFE

Room acoustic simulation of early reflections with the mirror-image method for arbitrary room geometries is an exponential problem, which, if computed to a high reflection order, will quickly exceed available computational and memory resources. The real-time Simulated Open Field Environment (rtSOFE) is freely available room-acoustic simulation and auralization software based on the mirror-image method. For auralization, rtSOFE synthesizes room impulse responses for each playback channel from the list of mirror images. A convolver program receives these seconds-long room impulse responses and convolves them for each sound source, sums across sources, and performs the playback equalization. Both programs perform computations in multiple threads in parallel using the OpenMP library. Many operations are implemented with Intel Advanced Vector Extensions (AVX) to increase processing speed. Image source trees are terminated after a definable number of invisible parent sources, keeping the number of computed mirrors manageable. With this, rtSOFE is capable of computing room impulse responses to high reflection orders in interactive real-time settings. I will present the software concept and discuss the approaches that speed up the computation.
End-to-end child-adult speech diarization in naturalistic conditions of preschool classrooms

Speech and language development are early indicators of overall analytical and learning ability in pre-school children. Early childhood researchers are interested in analyzing naturalistic versus controlled lab recordings to assess both quality and quantity of such communication interactions between children and adults/teachers. Unfortunately, present-day speech technologies are not capable of addressing the wide dynamic scenario of early childhood classroom settings. Due to diversity of acoustic events/conditionsin daylong audio streams, automated speaker diarization technology is limited and must be advanced to address this challenging domain for audio segmentation and meta-data information extraction. We investigate a Deep Learning-based diarization solution for segmenting classroom interactions of 3–5 year-old children engaging with teachers. Here, the focus is on speaker-label diarization which classifies speech segments as belonging to either Adults or Children, partitioned across multiple classrooms. Our proposed ECAPA-TDNN model achieves a best F1-score of 65.5% on data from two classrooms, based on open dev and test sets for each classroom. Also, F1-scores for individual speaker labels provide a breakdown of performance across naturalistic child classroom engagement. The study demonstrates the prospects of addressing educational assessment needs through communication audio stream analysis, while maintaining both security and privacy of all children and adults.
Coupled simulation of vibration and sound field of Stradivari violin

The aim of this study is to clarify the relationship between the vibration of violins made by Antonio Stradivari and the acoustic radiation around it. The highly precise geometry of the violin was determined with a micro-computed tomography scanner, and the material properties of the violin body (spruce and maple) were set as the orthotropic properties in the numerical simulation. Then, the numerical simulations coupling the vibrations by a forced oscillation on a bridge with the acoustic field pressure around the violin were performed. Furthermore, the sound pressure emanating in a huge rectangular box was successfully calculated by large scale computing and the open-source parallel acoustic analysis software.
Investigating the Use of Natural Language Processing for Automated Code Generation

Automated code generation has gained significant attention in recent years as a promising approach to streamline software development processes and improve productivity. Natural Language Processing (NLP), a subfield of artificial intelligence, has shown great potential for enabling automated code generation by extracting meaningful information from human-readable text and translating it into executable code. This paper presents a comprehensive investigation into the use of NLP techniques for automated code generation, including the challenges, opportunities, and current state-of-the-art approaches in this field.The paper begins with an overview of the motivation behind the use of NLP for automated code generation, highlighting the benefits it can offer to software development, such as reducing coding effort, improving code quality, and enhancing developer productivity. The challenges and limitations of using NLP for code generation are discussed, including issues related to ambiguity, context sensitivity, and code complexity.Next, the paper presents a review of the existing literature and state-of-the-art approaches in NLP-based code generation, including techniques such as semantic parsing, program synthesis, and code summarization. The strengths and weaknesses of these approaches are analysed, along with their potential use cases and limitations.The paper also discusses the evaluation methodologies and metrics used in assessing the effectiveness of NLP-based code generation techniques, including accuracy, efficiency, and code quality. The importance of benchmark datasets and the need for standardized evaluation practices are emphasized.Furthermore, the paper identifies the key research directions and future prospects of NLP-based code generation, including the integration of machine learning and deep learning techniques, the exploration of multilingual and multimodal NLP approaches, and the development of hybrid approaches combining NLP with other automated code generation methods.Finally, the paper concludes with a summary of the findings and recommendations for future research in this area, highlighting the potential impact of NLP on automated code generation and its implications for the software development industry.
A high content image-based assay for detection and measurement of SARS-CoV-2 neutralizing antibodies

Introduction:The pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has presented an urgency for neutralizing assays.The reference assay, plaque reduction neutralization test (PRNT), is not safe and requires a biosafety level-3 laboratory, since it demand for live viruses.Moreover, PRNT is laborious, time consuming and is not adapted to high throughput, a feature necessary to screen a high number of plasms, small molecules and antibodies.Objectives: Our aim was to develop a safe, easy and faster neutralizing antibodies (nAbs) assay.Methodology: Lentiviral and non-replicative particles (pseudovirus) were developed in HEK293T cells and used to transduce HEK293T-ACE2 overexpressing cells.The automated image analysis was standardized in a high content system, in which the average ZsGreen fluorescence intensity was used to select transduced and non-transduced cells, while the percentage of ZsGreen cells was the readout to calculate the potency of human plasmas (pNT50).Plasma samples from 29 healthy individuals were collected before and after vaccination. Results:The pseudovirus produced was specific for ACE2 overexpressing cells.It was observed a linear correlation between the percentage of ZsGreen + cells and ZsGreen average fluorescence intensity with viral titer.Pearson correlation coefficient was 0.938 and 0.997, respectively.The cut-off was calculated from the mean of positive control for neutralization plus 3 times the standard deviation, resulting in 0.52% and 14.19, respectively.Before vaccination, individuals presented 60-70% of neutralizing capacity.Thirty days after the first dose, there was a significative increasing of neutralizing capacity in individuals vaccinated with BNT162b2 and ChAdOx1 (*p< 0.0284 and ****p<0.0001,respectively), but no difference with CoronaVac.Thirty days after the second dose, all vaccinated presented a high neutralizing capacity of 85-90%.These percentages comprising an increasing of approximately 20% of nAbs compared to before vaccination (***p<0.0009for CoronaVac and ****p<0.0001for BNT162b2 and ChAdOx1). Conclusion:We developed and validated an image-based high content assay, which is safe, highthroughput compatible and able to determine the plasm neutralizing potency in two days.
Application of wavelet wransform wechniques to measured data

Wavelet transform theory includes a powerful and abstract set of signal processing tools. Students often understand the underlying mathematics of how to perform the procedures but lack the conceptual and computational understanding of how and when to apply wavelets to measured data. In this talk, we will discuss a project designed to take what is learned from textbooks and classroom lectures and apply these skills and tools to measured data from the field. The project is to cover an entire semester with multiple points for instructor evaluation and student revision. The end goal is that students will learn more signal processing skills through computational methods and data analysis and will produce a paper that can be part of a thesis/dissertation as well as a presentation at a future meeting.
Entropy in Science and How It Applies to Vocalization

Entropy is a scientific measure of uncertainty or irregularity in a system. It applies to the transmission of a signal, in particular the transmission of music and speech to a listener. This article explains entropy from a universal physical point of view. Beginning with the perspectives of famous scientists like Boltzmann and Shannon, some applications to voice and speech are given.
Perception error variation in masking contexts

Masking noise is an important tool in speech and hearing experiments. It provides a means to simulate real world conditions and provide misperceptions that are as naturally elicited as possible. However, the exact characteristics of the variable approach to this parameter is poorly understood. The present investigation explores the types and variability of errors across different noise conditions. The English Consistent Confusion Corpus [Marxer et al., JASA 140, EL458–EL463 (2016)], which provides data on consistently reproducible mispercepts, was used for this investigation. The dataset contains tokens of words in multiple masking conditions: speech shaped noise; three talker babble modulated noise; and four-talker natural babble, correlated with signal to noise ratios that bias a listener towards a specific mispercept. This dataset contains 3200 individual word tokens from four different speakers with 15 listeners. The interaction between types and variability of misperception with the different types of masking noise is explored. As well, the suitability of the different noise conditions for speech perception experiments is discussed.
Using machine learning to determine the best wavelet for compressing acoustic data

This research uses acoustic data to determine the best method to find the optimal wavelet to use for data compression. The power spectral densities before and after wavelet decomposition, compression, and decomposition (decompression) are compared to assess the quality of the compression. Machine learning methods are tested to choose the optimal wavelet with the best qualities. Ultimately, this research will create a program to automatically give the user the best wavelet(s) to compress a particular acoustic data set. [This research is funded by the University of New Orleans Office of Research.]
Fearless Steps APOLLO: Challenges in keyword spotting and topic detection for naturalistic audio streams

Fearless Steps (FS) APOLLO is a + 50,000 hr audio resource established by CRSS-UTDallas capturing all communications between NASA-MCC personnel, backroom staff, and Astronauts across manned Apollo Missions. Such a massive audio resource without metadata/unlabeled corpus provides limited benefit for communities outside Speech-and-Language Technology (SLT). Supplementing this audio with rich metadata developed using robust automated mechanisms to transcribe and highlight naturalistic communications can facilitate open research opportunities for SLT, speech sciences, education, and historical archival communities. In this study, we focus on customizing keyword spotting (KWS) and topic detection systems as an initial step towards conversational understanding. Extensive research in automatic speech recognition (ASR), speech activity, and speaker diarization using manually transcribed 125 h FS Challenge corpus has demonstrated the need for robust domain-specific model development. A major challenge in training KWS systems and topic detection models is the availability of word-level annotations. Forced alignment schemes evaluated using state-of-the-art ASR show significant degradation in segmentation performance. This study explores challenges in extracting accurate keyword segments using existing sentence-level transcriptions and proposes domain-specific KWS-based solutions to detect conversational topics in audio streams. [Work Sponsored by NSF via Grant No. 2016725 and EU’s Horizon 2021 R&amp;I Program under MSC Grant No. 101062614.]
Data Privacy in India Technical and Legal Perspective

This comprehensive paper presents an extensive analysis of the data privacy landscape in India, with a focus on both technical and legal perspectives. It provides an overview of the existing legal framework and regulatory environment in India, along with an evaluation of industry practices. The paper also examines the technical aspects of data privacy, including data encryption, anonymization, and localization requirements, and their effectiveness in safeguarding personal data. Furthermore, the paper explores the challenges faced by Indian organizations in implementing data privacy measures and suggests best practices to overcome these challenges. By offering valuable insights into the current state of data privacy in India, this paper provides recommendations for enhancing data protection practices, thereby benefiting individuals, organizations, and society at large.
Using standards-based assessment and reporting in a general education acoustics course

Standards-based assessment and reporting is a method of grading in which each student is responsible for demonstrating mastery of a set of course objectives called standards. In this course, students are assessed on each standard using short assessments that resemble a quiz, usually one or two pages long. Each standard is assigned a rating on a 5-step scale. The student's ratings throughout the semester determine the final grade the student will earn. If a student is not satisfied with a rating on an assessment, they are allowed to reassess the standard to demonstrate they have developed a fuller understanding of the topic. This method of student assessment is intended to encourage students to develop intrinsic motivation for learning the course material. Students are also not penalized for having a bad exam or quiz score due to unforeseen circumstances. Students often report that they need time at the start of the semester to get used to the grading system but that they ultimately see the benefit to their learning.
Case study of a Brothertown Indian Nation cultural heritage site—Toward a framework for acoustics heritage research in simulation, analysis, and auralization

The Brothertown Indian Nation and their ancestors have a centuries old heritage of group singing beginning along the Northeast Atlantic coast of what is now the United States and following them along their migration to the Midwest. The 18th century Curricomp cabin still stands in Connecticut and was an important location in the singing history and political movement that built Brothertown (Eeyamquittoowauconnuck). In this paper, a collaboration with Brothertown Indian Nation and an ongoing public humanities project regarding the Tribe’s aural traditions, a case study will be presented that investigates (1) the results of acoustic modeling and simulation of the Curricomp cabin and (2) auralizations for both binaural listening and a spatial audio installation using those models. Significantly, this paper employs a novel theoretical framework for acoustic heritage research that allows for (3) an analysis of how the scientific process and technological practices mediate intangible heritage. The acoustic models and auralization techniques created in this case study provide acoustic access to a heritage location that would otherwise be inaccessible to the Brothertown Community. The auralizations used in the spatial audio installation serve as a public humanities tool to amplify the contemporary voice of the Brothertown Indian Nation.
Research on intention tendency detection for Chinese medical question answering task

In the Chinese medical question and answer task, question intention detection is a very important part. At present, the common intention detection methods mainly use the manually designed matching rules to find the problem features to detect the intention of the problem, but the use of a large amount of labor usually brings about problems such as high cost and poor versatility. A novel method of intention detection is proposed in this paper. First, the collected questions with different intention categories are used to construct intention feature words. Then, based on the BERT pre-training language model, a two-classification model of phrase similarity is constructed. By comparing the combination results of problem word segmentation and the similarity of intention feature words, the multi-classification problem of problem intention detection is transformed into a two-classification problem between multiple phrases. Then we can get the inclination of the question for each intention category, that is the intention category of the question. The experiment shows that the method based on the two-classification model of phrase similarity has better effect than the previous methods, and the F1 value in the test set reaches 90.1.
Task Adaptive Multi-learner Network for Joint CTR and CVR Estimation

CTR and CVR are critical factors in personalized applications, and many methods jointly estimate them via multi-task learning to alleviate the ultra-sparsity of conversion behaviors. However, it is still difficult to predict CVR accurately and robustly due to the limited and even biased knowledge extracted by the single model tower optimized on insufficient conversion samples. In this paper, we propose a task adaptive multi-learner (TAML) framework for joint CTR and CVR prediction. We design a hierarchical task adaptive knowledge representation module with different experts to capture knowledge in different granularities, which can effectively exploit the commonalities between CTR and CVR estimation tasks meanwhile keeping their unique characteristics. We apply multiple learners to extract data knowledge from various views and fuse their predictions to obtain accurate and robust scores. To facilitate knowledge sharing across learners, we further perform self-distillation that uses the fused scores to teach different learners. Thorough offline and online experiments show the superiority of TAML in different Ad ranking tasks, and we have deployed it in Huawei’s online advertising platform to serve the main traffic.
Characteristics, Mechanisms of Action and Reflections of the Educational Metaverse

&lt;p&gt;The concept of the metaverse has gradually become a hot topic. With the continuous development of science and technology, people are trying to explore the development trend and possible application of the metaverse in the future world. With the continuous development of science and technology, people are trying to explore the development trend and possible application of the metaverse in the future world. The article gives a detailed explanation of the characteristics of the education metaverse and the mechanism of action of the education metaverse, which is considered to be one of the main application scenarios of the Metaverse. The article gives a detailed explanation of the characteristics of the education metaverse and the mechanism of action of the education metaverse, and reflects on the new teaching form of the education metaverse. From the perspective of critical reflection, it emphasizes adherence to reality-oriented, and people-oriented, and explores the two-way promotion of education and the metaverse.&lt;/p&gt;
Kendall’s tau estimator for bivariate zero-inflated count data

This paper extends the work of Pimentel et al. (2015), presenting an estimator of Kendall’s τ for bivariate zero-inflated count data. We provide achievable bounds of our proposed estimator and suggest how to estimate them, thereby making the estimator useful in practice.