1,720,958 research outputs found
Annotated-VocalSet: A Singing Voice Dataset
No full textThis dataset provides annotations for the VocalSet dataset, which is available online at
https://doi.org/10.5281/zenodo.1442513
.
The annotations generated for the VocalSet audio files include fundamental frequency contour, note onset, note offset, the transition between notes, note F0, note duration, Midi pitch, and lyrics.
VocalSet consists of more than 10 hours of monophonic recorded audio of professional singers in a variety of vocal techniques (n = 17) and several singers (m = 20) with several WAV files (p = 3560). However, although several categories, including techniques, singers, tempo, and loudness, are considered in the dataset, the sung notes were not annotated. Therefore, this dataset aims to annotate VocalSet to make it a more powerful dataset for researchers.
Details of the dataset are provided in the following academic journal paper.
Faghih, Behnam, and Joseph Timoney. 2022. "Annotated-VocalSet: A Singing Voice Dataset" Applied Sciences 12, no. 18: 9257. https://doi.org/10.3390/app12189257
Please use the above paper to cite this dataset
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Modelling Professional Singers: A Bayesian Machine Learning Approach with Enhanced Real-time Pitch Contour Extraction and Onset Processing from an Extended Dataset.
Get PDFSinging signals are one of the input data that computer systems need to analyse, and singing is part of all the cultures in the world. However, although there have been several studies on audio signal processing during the last three decades, it is still an active research area because most of the available algorithms in the literature require improvement due to the complexity of audio/music signals. More efforts are needed for analysing sounds/music in a real-time environment since the algorithms should work only on the past data, while in an offline system, all the required data are available. In addition, the complexity of the data will be increased if the audio signals come from singing due to the unique features of singing signals (such as vocal system, vibration, pitch drift, and tuning approach) that make the signals different and more complicated than those from an instrument.
This thesis is mainly focused on analysing singing signals and better understanding how trained- professional singers sing the pitch frequency and duration of the notes according to their position in a piece of music and the singing technique applied. To do this, it is discovered that by incorporating singing features, such as gender and BPM, a real-time pitch detection algorithm can be found to estimate fundamental frequencies with fewer errors. In addition, two novel algorithms were proposed, one for smoothing pitch contours and another for estimating onset, offset, and the transition between notes. These two algorithms showed better results as compared to several other state-of-the-art algorithms. Moreover, a new vocal dataset that included several annotations for 2688 singing files was published. Finally, this thesis presents two models for calculating pitches and the duration of notes according to their positions in a piece of music. In conclusion, optimizing results for pitch-oriented Music Information Retrieval (MIR) algorithms necessitates adapting/selecting them based on the unique characteristics of the signals. Achieving a universal algorithm that performs exceptionally well on all data types remains a formidable challenge given the current state of technology
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Modelling Professional Singers: A Bayesian Machine Learning Approach with Enhanced Real-time Pitch Contour Extraction and Onset Processing from an Extended Dataset.
No full textSinging signals are one of the input data that computer systems need to analyse, and singing is part of all the cultures in the world. However, although there have been several studies on audio signal processing during the last three decades, it is still an active research area because most of the available algorithms in the literature require improvement due to the complexity of audio/music signals. More efforts are needed for analysing sounds/music in a real-time environment since the algorithms should work only on the past data, while in an offline system, all the required data are available. In addition, the complexity of the data will be increased if the audio signals come from singing due to the unique features of singing signals (such as vocal system, vibration, pitch drift, and tuning approach) that make the signals different and more complicated than those from an instrument.
This thesis is mainly focused on analysing singing signals and better understanding how trained- professional singers sing the pitch frequency and duration of the notes according to their position in a piece of music and the singing technique applied. To do this, it is discovered that by incorporating singing features, such as gender and BPM, a real-time pitch detection algorithm can be found to estimate fundamental frequencies with fewer errors. In addition, two novel algorithms were proposed, one for smoothing pitch contours and another for estimating onset, offset, and the transition between notes. These two algorithms showed better results as compared to several other state-of-the-art algorithms. Moreover, a new vocal dataset that included several annotations for 2688 singing files was published. Finally, this thesis presents two models for calculating pitches and the duration of notes according to their positions in a piece of music. In conclusion, optimizing results for pitch-oriented Music Information Retrieval (MIR) algorithms necessitates adapting/selecting them based on the unique characteristics of the signals. Achieving a universal algorithm that performs exceptionally well on all data types remains a formidable challenge given the current state of technology
Modelling Professional Singers: A Bayesian Machine Learning Approach with Enhanced Real-time Pitch Contour Extraction and Onset Processing from an Extended Dataset.
No full textSinging signals are one of the input data that computer systems need to analyse, and singing is part of all the cultures in the world. However, although there have been several studies on audio signal processing during the last three decades, it is still an active research area because most of the available algorithms in the literature require improvement due to the complexity of audio/music signals. More efforts are needed for analysing sounds/music in a real-time environment since the algorithms should work only on the past data, while in an offline system, all the required data are available. In addition, the complexity of the data will be increased if the audio signals come from singing due to the unique features of singing signals (such as vocal system, vibration, pitch drift, and tuning approach) that make the signals different and more complicated than those from an instrument.
This thesis is mainly focused on analysing singing signals and better understanding how trained- professional singers sing the pitch frequency and duration of the notes according to their position in a piece of music and the singing technique applied. To do this, it is discovered that by incorporating singing features, such as gender and BPM, a real-time pitch detection algorithm can be found to estimate fundamental frequencies with fewer errors. In addition, two novel algorithms were proposed, one for smoothing pitch contours and another for estimating onset, offset, and the transition between notes. These two algorithms showed better results as compared to several other state-of-the-art algorithms. Moreover, a new vocal dataset that included several annotations for 2688 singing files was published. Finally, this thesis presents two models for calculating pitches and the duration of notes according to their positions in a piece of music. In conclusion, optimizing results for pitch-oriented Music Information Retrieval (MIR) algorithms necessitates adapting/selecting them based on the unique characteristics of the signals. Achieving a universal algorithm that performs exceptionally well on all data types remains a formidable challenge given the current state of technology
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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