3 Search Results for "Schuller, Bj�rn"

Document
DOI: 10.4230/DagRep.11.8.76
Conversational Agent as Trustworthy Autonomous System (Trust-CA) (Dagstuhl Seminar 21381)

Authors: Effie Lai-Chong Law, Asbjørn Følstad, Jonathan Grudin, and Björn Schuller

Published in: Dagstuhl Reports, Volume 11, Issue 8 (2022)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 21381 "Conversational Agent as Trustworthy Autonomous System (Trust-CA)". First, we present the abstracts of the talks delivered by the Seminar’s attendees. Then we report on the origin and process of our six breakout (working) groups. For each group, we describe its contributors, goals and key questions, key insights, and future research. The themes of the groups were derived from a pre-Seminar survey, which also led to a list of suggested readings for the topic of trust in conversational agents. The list is included in this report for references.
Cite as

Effie Lai-Chong Law, Asbjørn Følstad, Jonathan Grudin, and Björn Schuller. Conversational Agent as Trustworthy Autonomous System (Trust-CA) (Dagstuhl Seminar 21381). In Dagstuhl Reports, Volume 11, Issue 8, pp. 76-114, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@Article{law_et_al:DagRep.11.8.76,
  author =	{Law, Effie Lai-Chong and F{\o}lstad, Asbj{\o}rn and Grudin, Jonathan and Schuller, Bj\"{o}rn},
  title =	{{Conversational Agent as Trustworthy Autonomous System (Trust-CA) (Dagstuhl Seminar 21381)}},
  pages =	{76--114},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2022},
  volume =	{11},
  number =	{8},
  editor =	{Law, Effie Lai-Chong and F{\o}lstad, Asbj{\o}rn and Grudin, Jonathan and Schuller, Bj\"{o}rn},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.11.8.76},
  URN =		{urn:nbn:de:0030-drops-157702},
  doi =		{10.4230/DagRep.11.8.76},
  annote =	{Keywords: Conversational agents, Trust, Trustworthiness, Autonomous Systems}
}
Document
DOI: 10.4230/DagRep.3.11.1
Computational Audio Analysis (Dagstuhl Seminar 13451)

Authors: Meinard Müller, Shrikanth S. Narayanan, and Björn Schuller

Published in: Dagstuhl Reports, Volume 3, Issue 11 (2014)

Abstract
Compared to traditional speech, music, or sound processing, the computational analysis of general audio data has a relatively young research history. In particular, the extraction of affective information (i.e., information that does not deal with the 'immediate' nature of the content such as the spoken words or note events) from audio signals has become an important research strand with a huge increase of interest in academia and industry. At an early stage of this novel research direction, many analysis techniques and representations were simply transferred from the speech domain to other audio domains. However, general audio signals (including their affective aspects) typically possess acoustic and structural characteristics that distinguish them from spoken language or isolated `controlled' music or sound events. In the Dagstuhl Seminar 13451 titled "Computational Audio Analysis" we discussed the development of novel machine learning as well as signal processing techniques that are applicable for a wide range of audio signals and analysis tasks. In particular, we looked at a variety of sounds besides speech such as music recordings, animal sounds, environmental sounds, and mixtures thereof. In this report, we give an overview of the various contributions and results of the seminar. We start with an executive summary, which describes the main topics, goals, and group activities. Then, one finds a list of abstracts giving a more detailed overview of the participants' contributions as well as of the ideas and results discussed in the group meetings of our seminar. To conclude, an attempt is made to define the field as given by the views of the participants.
Cite as

Meinard Müller, Shrikanth S. Narayanan, and Björn Schuller. Computational Audio Analysis (Dagstuhl Seminar 13451). In Dagstuhl Reports, Volume 3, Issue 11, pp. 1-28, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2014)

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@Article{muller_et_al:DagRep.3.11.1,
  author =	{M\"{u}ller, Meinard and Narayanan, Shrikanth S. and Schuller, Bj\"{o}rn},
  title =	{{Computational Audio Analysis (Dagstuhl Seminar 13451)}},
  pages =	{1--28},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2014},
  volume =	{3},
  number =	{11},
  editor =	{M\"{u}ller, Meinard and Narayanan, Shrikanth S. and Schuller, Bj\"{o}rn},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.3.11.1},
  URN =		{urn:nbn:de:0030-drops-44346},
  doi =		{10.4230/DagRep.3.11.1},
  annote =	{Keywords: Audio Analysis, Signal Processing, Machine Learning, Sound, Speech, Music, Affective Computing}
}
Document
DOI: 10.4230/DFU.Vol3.11041.195
Music Information Retrieval: An Inspirational Guide to Transfer from Related Disciplines

Authors: Felix Weninger, Björn Schuller, Cynthia C.S. Liem, Frank Kurth, and Alan Hanjalic

Published in: Dagstuhl Follow-Ups, Volume 3, Multimodal Music Processing (2012)

Abstract
The emerging field of Music Information Retrieval (MIR) has been influenced by neighboring domains in signal processing and machine learning, including automatic speech recognition, image processing and text information retrieval. In this contribution, we start with concrete examples for methodology transfer between speech and music processing, oriented on the building blocks of pattern recognition: preprocessing, feature extraction, and classification/decoding. We then assume a higher level viewpoint when describing sources of mutual inspiration derived from text and image information retrieval. We conclude that dealing with the peculiarities of music in MIR research has contributed to advancing the state-of-the-art in other fields, and that many future challenges in MIR are strikingly similar to those that other research areas have been facing.
Cite as

Felix Weninger, Björn Schuller, Cynthia C.S. Liem, Frank Kurth, and Alan Hanjalic. Music Information Retrieval: An Inspirational Guide to Transfer from Related Disciplines. In Multimodal Music Processing. Dagstuhl Follow-Ups, Volume 3, pp. 195-216, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)

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@InCollection{weninger_et_al:DFU.Vol3.11041.195,
  author =	{Weninger, Felix and Schuller, Bj\"{o}rn and Liem, Cynthia C.S. and Kurth, Frank and Hanjalic, Alan},
  title =	{{Music Information Retrieval: An Inspirational Guide to Transfer from Related Disciplines}},
  booktitle =	{Multimodal Music Processing},
  pages =	{195--216},
  series =	{Dagstuhl Follow-Ups},
  ISBN =	{978-3-939897-37-8},
  ISSN =	{1868-8977},
  year =	{2012},
  volume =	{3},
  editor =	{M\"{u}ller, Meinard and Goto, Masataka and Schedl, Markus},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DFU.Vol3.11041.195},
  URN =		{urn:nbn:de:0030-drops-34737},
  doi =		{10.4230/DFU.Vol3.11041.195},
  annote =	{Keywords: Feature extraction, machine learning, multimodal fusion, evaluation, human factors, cross-domain methodology transfer}
}
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