Example of EURASIP Journal on Audio, Speech, and Music Processing format
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Example of EURASIP Journal on Audio, Speech, and Music Processing format Example of EURASIP Journal on Audio, Speech, and Music Processing format Example of EURASIP Journal on Audio, Speech, and Music Processing format Example of EURASIP Journal on Audio, Speech, and Music Processing format Example of EURASIP Journal on Audio, Speech, and Music Processing format Example of EURASIP Journal on Audio, Speech, and Music Processing format Example of EURASIP Journal on Audio, Speech, and Music Processing format Example of EURASIP Journal on Audio, Speech, and Music Processing format Example of EURASIP Journal on Audio, Speech, and Music Processing format Example of EURASIP Journal on Audio, Speech, and Music Processing format Example of EURASIP Journal on Audio, Speech, and Music Processing format Example of EURASIP Journal on Audio, Speech, and Music Processing format Example of EURASIP Journal on Audio, Speech, and Music Processing format Example of EURASIP Journal on Audio, Speech, and Music Processing format Example of EURASIP Journal on Audio, Speech, and Music Processing format Example of EURASIP Journal on Audio, Speech, and Music Processing format Example of EURASIP Journal on Audio, Speech, and Music Processing format Example of EURASIP Journal on Audio, Speech, and Music Processing format
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Example of EURASIP Journal on Audio, Speech, and Music Processing format Example of EURASIP Journal on Audio, Speech, and Music Processing format Example of EURASIP Journal on Audio, Speech, and Music Processing format Example of EURASIP Journal on Audio, Speech, and Music Processing format Example of EURASIP Journal on Audio, Speech, and Music Processing format Example of EURASIP Journal on Audio, Speech, and Music Processing format Example of EURASIP Journal on Audio, Speech, and Music Processing format Example of EURASIP Journal on Audio, Speech, and Music Processing format Example of EURASIP Journal on Audio, Speech, and Music Processing format Example of EURASIP Journal on Audio, Speech, and Music Processing format Example of EURASIP Journal on Audio, Speech, and Music Processing format Example of EURASIP Journal on Audio, Speech, and Music Processing format Example of EURASIP Journal on Audio, Speech, and Music Processing format Example of EURASIP Journal on Audio, Speech, and Music Processing format Example of EURASIP Journal on Audio, Speech, and Music Processing format Example of EURASIP Journal on Audio, Speech, and Music Processing format Example of EURASIP Journal on Audio, Speech, and Music Processing format Example of EURASIP Journal on Audio, Speech, and Music Processing format
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open access Open Access

EURASIP Journal on Audio, Speech, and Music Processing — Template for authors

Publisher: Springer
Categories Rank Trend in last 3 yrs
Acoustics and Ultrasonics #15 of 43 down down by 2 ranks
Electrical and Electronic Engineering #307 of 693 down down by 69 ranks
journal-quality-icon Journal quality:
Good
calendar-icon Last 4 years overview: 89 Published Papers | 275 Citations
indexed-in-icon Indexed in: Scopus
last-updated-icon Last updated: 11/07/2020
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Related Journals

open access Open Access

IEEE

Quality:  
High
CiteRatio: 5.5
SJR: 1.159
SNIP: 1.672
open access Open Access

IEEE

Quality:  
High
CiteRatio: 6.4
SJR: 0.786
SNIP: 2.027
open access Open Access

IEEE

Quality:  
High
CiteRatio: 3.7
SJR: 0.396
SNIP: 1.133

Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

1.289

4% from 2018

Impact factor for EURASIP Journal on Audio, Speech, and Music Processing from 2016 - 2019
Year Value
2019 1.289
2018 1.244
2017 3.057
2016 1.579
graph view Graph view
table view Table view

3.1

35% from 2019

CiteRatio for EURASIP Journal on Audio, Speech, and Music Processing from 2016 - 2020
Year Value
2020 3.1
2019 2.3
2018 3.3
2017 2.7
2016 2.5
graph view Graph view
table view Table view

insights Insights

  • Impact factor of this journal has increased by 4% in last year.
  • This journal’s impact factor is in the top 10 percentile category.

insights Insights

  • CiteRatio of this journal has increased by 35% in last years.
  • This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

0.259

10% from 2019

SJR for EURASIP Journal on Audio, Speech, and Music Processing from 2016 - 2020
Year Value
2020 0.259
2019 0.289
2018 0.296
2017 0.337
2016 0.275
graph view Graph view
table view Table view

1.101

9% from 2019

SNIP for EURASIP Journal on Audio, Speech, and Music Processing from 2016 - 2020
Year Value
2020 1.101
2019 1.012
2018 1.003
2017 1.015
2016 0.827
graph view Graph view
table view Table view

insights Insights

  • SJR of this journal has decreased by 10% in last years.
  • This journal’s SJR is in the top 10 percentile category.

insights Insights

  • SNIP of this journal has increased by 9% in last years.
  • This journal’s SNIP is in the top 10 percentile category.

EURASIP Journal on Audio, Speech, and Music Processing

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Springer

EURASIP Journal on Audio, Speech, and Music Processing

Approved by publishing and review experts on SciSpace, this template is built as per for EURASIP Journal on Audio, Speech, and Music Processing formatting guidelines as mentioned in Springer author instructions. The current version was created on and has been used by 855 authors to write and format their manuscripts to this journal.

i
Last updated on
11 Jul 2020
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ISSN
1687-4722
i
Open Access
Yes
i
Sherpa RoMEO Archiving Policy
Green faq
i
Plagiarism Check
Available via Turnitin
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Endnote Style
Download Available
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Citation Type
Author Year
(Blonder et al, 1982)
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Bibliography Example
Beenakker CWJ (2006) Specular andreev reflection in graphene. Phys Rev Lett 97(6):067,007, URL 10.1103/PhysRevLett.97.067007

Top papers written in this journal

open accessOpen access Journal Article DOI: 10.1186/1687-4722-2013-1
Context-dependent sound event detection
Toni Heittola1, Annamaria Mesaros1, Antti Eronen2, Tuomas Virtanen1

Abstract:

The work presented in this article studies how the context information can be used in the automatic sound event detection process, and how the detection system can benefit from such information. Humans are using context information to make more accurate predictions about the sound events and ruling out unlikely events given t... The work presented in this article studies how the context information can be used in the automatic sound event detection process, and how the detection system can benefit from such information. Humans are using context information to make more accurate predictions about the sound events and ruling out unlikely events given the context. We propose a similar utilization of context information in the automatic sound event detection process. The proposed approach is composed of two stages: automatic context recognition stage and sound event detection stage. Contexts are modeled using Gaussian mixture models and sound events are modeled using three-state left-to-right hidden Markov models. In the first stage, audio context of the tested signal is recognized. Based on the recognized context, a context-specific set of sound event classes is selected for the sound event detection stage. The event detection stage also uses context-dependent acoustic models and count-based event priors. Two alternative event detection approaches are studied. In the first one, a monophonic event sequence is outputted by detecting the most prominent sound event at each time instance using Viterbi decoding. The second approach introduces a new method for producing polyphonic event sequence by detecting multiple overlapping sound events using multiple restricted Viterbi passes. A new metric is introduced to evaluate the sound event detection performance with various level of polyphony. This combines the detection accuracy and coarse time-resolution error into one metric, making the comparison of the performance of detection algorithms simpler. The two-step approach was found to improve the results substantially compared to the context-independent baseline system. In the block-level, the detection accuracy can be almost doubled by using the proposed context-dependent event detection. read more read less

Topics:

Event (probability theory) (56%)56% related to the paper, Context (language use) (53%)53% related to the paper, Viterbi algorithm (53%)53% related to the paper, Hidden Markov model (50%)50% related to the paper
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217 Citations
open accessOpen access Journal Article DOI: 10.1186/1687-4722-2012-25
Comparative study of digital audio steganography techniques
Fatiha Djebbar1, Beghdad Ayad2, Karim Abed Meraim3, Habib Hamam4

Abstract:

The rapid spread in digital data usage in many real life applications have urged new and effective ways to ensure their security. Efficient secrecy can be achieved, at least in part, by implementing steganograhy techniques. Novel and versatile audio steganographic methods have been proposed. The goal of steganographic systems... The rapid spread in digital data usage in many real life applications have urged new and effective ways to ensure their security. Efficient secrecy can be achieved, at least in part, by implementing steganograhy techniques. Novel and versatile audio steganographic methods have been proposed. The goal of steganographic systems is to obtain secure and robust way to conceal high rate of secret data. We focus in this paper on digital audio steganography, which has emerged as a prominent source of data hiding across novel telecommunication technologies such as covered voice-over-IP, audio conferencing, etc. The multitude of steganographic criteria has led to a great diversity in these system design techniques. In this paper, we review current digital audio steganographic techniques and we evaluate their performance based on robustness, security and hiding capacity indicators. Another contribution of this paper is the provision of a robustness-based classification of steganographic models depending on their occurrence in the embedding process. A survey of major trends of audio steganography applications is also discussed in this paper. read more read less

Topics:

Digital audio (63%)63% related to the paper, Steganography (58%)58% related to the paper, Information hiding (51%)51% related to the paper
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175 Citations
open accessOpen access Journal Article DOI: 10.1186/S13636-015-0054-9
ViSQOL: an objective speech quality model
Andrew Hines1, Andrew Hines2, Jan Skoglund3, Anil Kokaram3, Naomi Harte2

Abstract:

This paper presents an objective speech quality model, ViSQOL, the Virtual Speech Quality Objective Listener. It is a signal-based, full-reference, intrusive metric that models human speech quality perception using a spectro-temporal measure of similarity between a reference and a test speech signal. The metric has been parti... This paper presents an objective speech quality model, ViSQOL, the Virtual Speech Quality Objective Listener. It is a signal-based, full-reference, intrusive metric that models human speech quality perception using a spectro-temporal measure of similarity between a reference and a test speech signal. The metric has been particularly designed to be robust for quality issues associated with Voice over IP (VoIP) transmission. This paper describes the algorithm and compares the quality predictions with the ITU-T standard metrics PESQ and POLQA for common problems in VoIP: clock drift, associated time warping, and playout delays. The results indicate that ViSQOL and POLQA significantly outperform PESQ, with ViSQOL competing well with POLQA. An extensive benchmarking against PESQ, POLQA, and simpler distance metrics using three speech corpora (NOIZEUS and E4 and the ITU-T P.Sup. 23 database) is also presented. These experiments benchmark the performance for a wide range of quality impairments, including VoIP degradations, a variety of background noise types, speech enhancement methods, and SNR levels. The results and subsequent analysis show that both ViSQOL and POLQA have some performance weaknesses and under-predict perceived quality in certain VoIP conditions. Both have a wider application and robustness to conditions than PESQ or more trivial distance metrics. ViSQOL is shown to offer a useful alternative to POLQA in predicting speech quality in VoIP scenarios. read more read less

Topics:

POLQA (79%)79% related to the paper, PESQ (63%)63% related to the paper, Speech enhancement (59%)59% related to the paper
View PDF
107 Citations
open accessOpen access Journal Article DOI: 10.1186/S13636-014-0047-0
Noisy training for deep neural networks in speech recognition
Shi Yin1, Shi Yin2, Chao Liu1, Zhiyong Zhang1, Yiye Lin3, Yiye Lin1, Dong Wang1, Javier Tejedor4, Thomas Fang Zheng1, Yin-Guo Li2

Abstract:

Deep neural networks (DNNs) have gained remarkable success in speech recognition, partially attributed to the flexibility of DNN models in learning complex patterns of speech signals. This flexibility, however, may lead to serious over-fitting and hence miserable performance degradation in adverse acoustic conditions such as ... Deep neural networks (DNNs) have gained remarkable success in speech recognition, partially attributed to the flexibility of DNN models in learning complex patterns of speech signals. This flexibility, however, may lead to serious over-fitting and hence miserable performance degradation in adverse acoustic conditions such as those with high ambient noises. We propose a noisy training approach to tackle this problem: by injecting moderate noises into the training data intentionally and randomly, more generalizable DNN models can be learned. This ‘noise injection’ technique, although known to the neural computation community already, has not been studied with DNNs which involve a highly complex objective function. The experiments presented in this paper confirm that the noisy training approach works well for the DNN model and can provide substantial performance improvement for DNN-based speech recognition. read more read less
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106 Citations
open accessOpen access Journal Article DOI: 10.1155/2010/546047
Automatic recognition of lyrics in singing
Annamaria Mesaros1, Tuomas Virtanen1

Abstract:

The paper considers the task of recognizing phonemes and words from a singing input by using a phonetic hidden Markov model recognizer. The system is targeted to both monophonic singing and singing in polyphonic music. A vocal separation algorithm is applied to separate the singing from polyphonic music. Due to the lack of an... The paper considers the task of recognizing phonemes and words from a singing input by using a phonetic hidden Markov model recognizer. The system is targeted to both monophonic singing and singing in polyphonic music. A vocal separation algorithm is applied to separate the singing from polyphonic music. Due to the lack of annotated singing databases, the recognizer is trained using speech and linearly adapted to singing. Global adaptation to singing is found to improve singing recognition performance. Further improvement is obtained by gender-specific adaptation. We also study adaptation with multiple base classes defined by either phonetic or acoustic similarity. We test phoneme-level and word-level n-gram language models. The phoneme language models are trained on the speech database text. The large-vocabulary word-level language model is trained on a database of textual lyrics. Two applications are presented. The recognizer is used to align textual lyrics to vocals in polyphonic music, obtaining an average error of 0.94 seconds for line-level alignment. A query-by-singing retrieval application based on the recognized words is also constructed; in 57% of the cases, the first retrieved song is the correct one. read more read less

Topics:

Singing (55%)55% related to the paper, Language model (54%)54% related to the paper
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97 Citations
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Frequently asked questions

1. Can I write EURASIP Journal on Audio, Speech, and Music Processing in LaTeX?

Absolutely not! Our tool has been designed to help you focus on writing. You can write your entire paper as per the EURASIP Journal on Audio, Speech, and Music Processing guidelines and auto format it.

2. Do you follow the EURASIP Journal on Audio, Speech, and Music Processing guidelines?

Yes, the template is compliant with the EURASIP Journal on Audio, Speech, and Music Processing guidelines. Our experts at SciSpace ensure that. If there are any changes to the journal's guidelines, we'll change our algorithm accordingly.

3. Can I cite my article in multiple styles in EURASIP Journal on Audio, Speech, and Music Processing?

Of course! We support all the top citation styles, such as APA style, MLA style, Vancouver style, Harvard style, and Chicago style. For example, when you write your paper and hit autoformat, our system will automatically update your article as per the EURASIP Journal on Audio, Speech, and Music Processing citation style.

4. Can I use the EURASIP Journal on Audio, Speech, and Music Processing templates for free?

Sign up for our free trial, and you'll be able to use all our features for seven days. You'll see how helpful they are and how inexpensive they are compared to other options, Especially for EURASIP Journal on Audio, Speech, and Music Processing.

5. Can I use a manuscript in EURASIP Journal on Audio, Speech, and Music Processing that I have written in MS Word?

Yes. You can choose the right template, copy-paste the contents from the word document, and click on auto-format. Once you're done, you'll have a publish-ready paper EURASIP Journal on Audio, Speech, and Music Processing that you can download at the end.

6. How long does it usually take you to format my papers in EURASIP Journal on Audio, Speech, and Music Processing?

It only takes a matter of seconds to edit your manuscript. Besides that, our intuitive editor saves you from writing and formatting it in EURASIP Journal on Audio, Speech, and Music Processing.

7. Where can I find the template for the EURASIP Journal on Audio, Speech, and Music Processing?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per EURASIP Journal on Audio, Speech, and Music Processing's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

8. Can I reformat my paper to fit the EURASIP Journal on Audio, Speech, and Music Processing's guidelines?

Of course! You can do this using our intuitive editor. It's very easy. If you need help, our support team is always ready to assist you.

9. EURASIP Journal on Audio, Speech, and Music Processing an online tool or is there a desktop version?

SciSpace's EURASIP Journal on Audio, Speech, and Music Processing is currently available as an online tool. We're developing a desktop version, too. You can request (or upvote) any features that you think would be helpful for you and other researchers in the "feature request" section of your account once you've signed up with us.

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After writing your paper autoformatting in EURASIP Journal on Audio, Speech, and Music Processing, you can download it in multiple formats, viz., PDF, Docx, and LaTeX.

12. Is EURASIP Journal on Audio, Speech, and Music Processing's impact factor high enough that I should try publishing my article there?

To be honest, the answer is no. The impact factor is one of the many elements that determine the quality of a journal. Few of these factors include review board, rejection rates, frequency of inclusion in indexes, and Eigenfactor. You need to assess all these factors before you make your final call.

13. What is Sherpa RoMEO Archiving Policy for EURASIP Journal on Audio, Speech, and Music Processing?

SHERPA/RoMEO Database

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for EURASIP Journal on Audio, Speech, and Music Processing. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour Archiving policy
Green Can archive pre-print and post-print or publisher's version/PDF
Blue Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow Can archive pre-print (ie pre-refereeing)
White Archiving not formally supported
FYI:
  1. Pre-prints as being the version of the paper before peer review and
  2. Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In EURASIP Journal on Audio, Speech, and Music Processing?

The 5 most common citation types in order of usage for EURASIP Journal on Audio, Speech, and Music Processing are:.

S. No. Citation Style Type
1. Author Year
2. Numbered
3. Numbered (Superscripted)
4. Author Year (Cited Pages)
5. Footnote

15. How do I submit my article to the EURASIP Journal on Audio, Speech, and Music Processing?

It is possible to find the Word template for any journal on Google. However, why use a template when you can write your entire manuscript on SciSpace , auto format it as per EURASIP Journal on Audio, Speech, and Music Processing's guidelines and download the same in Word, PDF and LaTeX formats? Give us a try!.

16. Can I download EURASIP Journal on Audio, Speech, and Music Processing in Endnote format?

Yes, SciSpace provides this functionality. After signing up, you would need to import your existing references from Word or Bib file to SciSpace. Then SciSpace would allow you to download your references in EURASIP Journal on Audio, Speech, and Music Processing Endnote style according to Elsevier guidelines.

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