| Discipline name | Position | Best Scientists | Publications | D-Index |
|---|---|---|---|---|
| Computer Science | 568 | 33 | 55 | 10 |
Eurasip Journal on Audio, Speech, and Music Processing focuses on Speech recognition, Artificial intelligence, Algorithm, Pattern recognition and Natural language processing. Some problems in Speech recognition that were presented in Eurasip Journal on Audio, Speech, and Music Processing overlapped with concepts under Speech enhancement and Robustness (computer science). The study of Noise serves as the foundation of the Speech enhancement research discussed in the journal.
The journal explores research in Artificial intelligence and the adjacent study of Computer vision. Topics in Algorithm were tackled in line with various other fields like Signal and Audio signal. The journal concentrated on Pattern recognition research, specifically Mel-frequency cepstrum, Support vector machine and Feature extraction.
Aside from research in Natural language processing, the journal also discusses Term (time) studies. The research on Hidden Markov model tackled can also make contributions to studies in the areas of Mixture model and Acoustic model.
The most cited articles are mainly concerned with subjects like Speech recognition, Artificial intelligence, Robustness (computer science), Hidden Markov model and Algorithm. The study on Speech recognition presented in the most cited publications is investigated in conjunction with research in Context (language use). The journal publications explore topics in Artificial intelligence which can be helpful for research in disciplines like Pattern recognition and Natural language processing.
The concepts of Speech recognition, Algorithm, Speech enhancement, Noise and Artificial intelligence are tackled in Eurasip Journal on Audio, Speech, and Music Processing. The Speech recognition research dealing mostly with Spectrogram is the focus of the journal. While Algorithm is the focus of the journal, it also provided insights into the studies of Time domain, Acoustic source localization, Signal, Direction of arrival and Estimator.
Issues in Speech enhancement were discussed, taking into consideration concepts from other disciplines like Microphone array, Noise (signal processing), Robustness (computer science) and Filter (signal processing). Topics in Noise explored in it were investigated in conjunction with research in Network architecture, Network performance, Reverberation and Deep neural networks. The research on Artificial intelligence featured in the journal combines topics in other fields like Frame (networking) and Pattern recognition.
A key indicator for each journal is its effectiveness in reaching other researchers with the papers published at that venue.
The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.
The top authors publishing in Eurasip Journal on Audio, Speech, and Music Processing (based on the number of publications) are:
The overall trend for top authors publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top authors.
Only papers with recognized affiliations are considered
The top affiliations publishing in Eurasip Journal on Audio, Speech, and Music Processing (based on the number of publications) are:
The overall trend for top affiliations publishing in this journal is outlined below. The chart shows the number of publications at each edition of the journal for top affiliations.
The publication chance index shows the ratio of articles published by the best research institutions in the journal edition to all articles published within that journal. The best research institutions were selected based on the largest number of articles published during all editions of the journal.
The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.
During the most recent 2021 edition, 2.86% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 38.24% were posted by at least one author from the top 10 institutions publishing in the journal. Another 5.88% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 20.59% of all publications and 35.29% were from other institutions.
A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of journals they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same journal from year to year.
The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the journal in relation to all participants in a given year.
The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.
Our experience to innovation index was created to show a cross-section of the experience level of authors publishing in a journal. The index includes the authors publishing at the last edition of a journal, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).
The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:
The chart below illustrates experience levels of first authors in cases of publications with multiple authors.
Pursuing a career in the research areas explored by the Eurasip Journal on Audio, Speech, and Music Processing can open up a range of professional opportunities. For instance, knowledge in areas such as Artificial Intelligence, Speech Recognition and Natural Language Processing is critical for fields such as teaching, particularly in states like Alabama where technology integration in classrooms is highly valued. Aspiring educators considering a career in this area might find our guide on how to become a teacher in Alabama with a bachelor's degree valuable. This will assist in understanding the requirements and prerequisites of advancing a career in education with a focus on AI and Speech Recognition.
Moreover, involving oneself in academic publications like Eurasip Journal on Audio, Speech, and Music Processing, can enhance one's academic and professional profile. Participation not only implies submitting research findings but also becoming part of the conversation on developing trends, theories and technologies. It aids in establishing a network with peer professionals and academics in the field.
Therefore, leveraging academic exploration and career growth opportunities associated with the research topics covered in this journal can offer significant professional benefits. Ultimately, it encourages a cycle of learning, innovation, and progress in the burgeoning field of audio, speech and music processing.
Chunyan Ji;Thosini Bamunu Mudiyanselage;Yutong Gao;Yi Pan
(2021)Loris Nanni;Yandre M. G. Costa;Rafael de Lima Aguiar;Rafael B. Mangolin
(2020)Diego Di Carlo;Pinchas Tandeitnik;Cedrić Foy;Nancy Bertin
(2021)Shahin Amiriparian;Maurice Gerczuk;Sandra Ottl;Lukas Stappen
(2020)Junfeng Hou;Wu Guo;Yan Song;Li-Rong Dai
(2020)Tobias Gburrek;Joerg Schmalenstroeer;Reinhold Haeb-Umbach
(2021)Usama Saqib;Usama Saqib;Sharon Gannot;Jesper Rindom Jensen
(2020)Rajat Hebbar;Pavlos Papadopoulos;Ramon Reyes;Alexander F. Danvers
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