33rd International Workshop on Machine Learning for Signal Processing

Top Research Topics at International Workshop on Machine Learning for Signal Processing?

• Artificial intelligence (58.58%)
• Pattern recognition (38.52%)
• Algorithm (21.43%)

International Workshop on Machine Learning for Signal Processing primarily tackles Artificial intelligence, Pattern recognition, Algorithm, Machine learning and Speech recognition. The work tackled in International Workshop on Machine Learning for Signal Processing goes beyond the discipline of Artificial intelligence as it also encompasses Computer vision. Issues in Pattern recognition were discussed, taking into consideration concepts from other disciplines like Contextual image classification and Cluster analysis.

The conference focuses on Algorithm but the discussions also offer insight into other areas such as Mathematical optimization, Gaussian process and Blind signal separation. The in-depth study on Blind signal separation also explores topics in the intersecting field of Source separation. Many of the studies tackled connect Machine learning with a similar field of study like Data mining.

It dives deep in exploring the relationship between the study of Speech recognition and Electroencephalography. The study on Feature extraction presented in the event intersects with subjects under the field of Feature (computer vision). The Deep learning study featured in it draws connections with the study of Convolutional neural network.

What are the most cited papers published at the conference?

• Environmental sound classification with convolutional neural networks (407 citations)
• ITEM2VEC: Neural item embedding for collaborative filtering (212 citations)
• Stochastic triplet embedding (160 citations)

Research areas of the most cited articles at International Workshop on Machine Learning for Signal Processing:

The published articles mainly tackle studies in Artificial intelligence, Pattern recognition, Speech recognition, Machine learning and Algorithm. While Pattern recognition is the focus of the most cited articles, it also provides insights into the studies of Noise measurement, Non-negative matrix factorization, Blind signal separation and Electroencephalography. The published articles hold forums on Speech recognition that merge themes from other disciplines such as Audio signal processing, Mel-frequency cepstrum and Time–frequency analysis.

What topics the last edition of the conference is best known for?

• Artificial intelligence
• Statistics
• Machine learning

The previous edition focused in particular on these issues:

The conference investigates areas of study like Artificial intelligence, Pattern recognition, Algorithm, Artificial neural network and Feature extraction. The Artificial intelligence study tackled is a key component of adjacent topics in the area of Machine learning. While Pattern recognition is the focus of it, it also provided insights into the studies of Time–frequency analysis, MNIST database, Robustness (computer science) and Spectrogram.

The studies on Algorithm discussed can also contribute to research in the domains of Sparse matrix, Signal processing, Blind signal separation and Time series. International Workshop on Machine Learning for Signal Processing tackles studies in Speech enhancement and the interrelated subject of Signal-to-noise ratio to gain insights into Artificial neural network. Feature extraction research featured in the event incorporates concerns from various other topics such as Network architecture, Feature (machine learning), Feature (computer vision), Contextual image classification and Scatternet.

The most cited articles from the last conference are:

• Complex spectrogram enhancement by convolutional neural network with multi-metrics learning (93 citations)
• Deep convolutional neural networks for interpretable analysis of EEG sleep stage scoring (62 citations)
• A recurrent encoder-decoder approach with skip-filtering connections for monaural singing voice separation (34 citations)

Papers citation over time

A key indicator for each conference 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.

Top authors and change over time

The top authors publishing at International Workshop on Machine Learning for Signal Processing (based on the number of publications) are:

• Simo Särkkä (5 papers) published 5 papers at the last edition,
• Vince D. Calhoun (3 papers) published 3 papers at the last edition,
• Jing Sui (3 papers) published 3 papers at the last edition,
• Tianzi Jiang (3 papers) published 3 papers at the last edition,
• Kazuyoshi Yoshii (3 papers) published 3 papers at the last edition.

The overall trend for top authors publishing at this conference is outlined below. The chart shows the number of publications at each edition of the conference for top authors.

Top affiliations and change over time

Only papers with recognized affiliations are considered

The top affiliations publishing at International Workshop on Machine Learning for Signal Processing (based on the number of publications) are:

• National Chiao Tung University (5 papers) published 5 papers at the last edition,
• Aalto University (5 papers) published 5 papers at the last edition,
• Chinese Academy of Sciences (3 papers) published 3 papers at the last edition,
• Technical University of Denmark (3 papers) published 3 papers at the last edition,
• University of Cambridge (3 papers) published 3 papers at the last edition.

The overall trend for top affiliations publishing at this conference is outlined below. The chart shows the number of publications at each edition of the conference for top affiliations.

Publication chance based on affiliation

The publication chance index shows the ratio of articles published by the best research institutions at the conference edition to all articles published within that conference. The best research institutions were selected based on the largest number of articles published during all editions of the conference.

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 2017 edition, 3.26% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 32.58% were posted by at least one author from the top 10 institutions publishing at the conference. Another 22.47% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 24.72% of all publications and 20.22% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of conferences they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same conference 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 conference in relation to all participants in a given year.

Returning Institution Index

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.

The experience to innovation index

Our experience to innovation index was created to show a cross-section of the experience level of authors publishing at a conference. The index includes the authors publishing at the last edition of a conference, 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:

• Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
• Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
• Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
• Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
• Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.