2 Search Results for "Serrà, Joan"

Document
DOI: 10.4230/LIPIcs.ECRTS.2022.20
Using Markov’s Inequality with Power-Of-k Function for Probabilistic WCET Estimation

Authors: Sergi Vilardell, Isabel Serra, Enrico Mezzetti, Jaume Abella, Francisco J. Cazorla, and Joan del Castillo

Published in: LIPIcs, Volume 231, 34th Euromicro Conference on Real-Time Systems (ECRTS 2022)

Abstract
Deriving WCET estimates for software programs with probabilistic means (a.k.a. pWCET estimation) has received significant attention during last years as a way to deal with the increased complexity of the processors used in real-time systems. Many works build on Extreme Value Theory (EVT) that is fed with a sample of the collected data (execution times). In its application, EVT carries two sources of uncertainty: the first one that is intrinsic to the EVT model and relates to determining the subset of the sample that belongs to the (upper) tail, and hence, is actually used by EVT for prediction; and the second one that is induced by the sampling process and hence is inherent to all sample-based methods. In this work, we show that Markov’s inequality can be used to obtain provable trustworthy probabilistic bounds to the tail of a distribution without incurring any model-intrinsic uncertainty. Yet, it produces pessimistic estimates that we shave substantially by proposing the use of a power-of-k function instead of the default identity function used by Markov’s inequality. Lastly, we propose a method to deal with sampling uncertainty for Markov’s inequality that consistently improves EVT estimates on synthetic and real data obtained from a railway application.
Cite as

Sergi Vilardell, Isabel Serra, Enrico Mezzetti, Jaume Abella, Francisco J. Cazorla, and Joan del Castillo. Using Markov’s Inequality with Power-Of-k Function for Probabilistic WCET Estimation. In 34th Euromicro Conference on Real-Time Systems (ECRTS 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 231, pp. 20:1-20:24, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{vilardell_et_al:LIPIcs.ECRTS.2022.20,
  author =	{Vilardell, Sergi and Serra, Isabel and Mezzetti, Enrico and Abella, Jaume and Cazorla, Francisco J. and del Castillo, Joan},
  title =	{{Using Markov’s Inequality with Power-Of-k Function for Probabilistic WCET Estimation}},
  booktitle =	{34th Euromicro Conference on Real-Time Systems (ECRTS 2022)},
  pages =	{20:1--20:24},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-239-6},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{231},
  editor =	{Maggio, Martina},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.ECRTS.2022.20},
  URN =		{urn:nbn:de:0030-drops-163377},
  doi =		{10.4230/LIPIcs.ECRTS.2022.20},
  annote =	{Keywords: Markov’s inequality, probabilistic time estimates, probabilistic WCET, Extreme Value Theory}
}
Document
DOI: 10.4230/DFU.Vol3.11041.157
Audio Content-Based Music Retrieval

Authors: Peter Grosche, Meinard Müller, and Joan Serrà

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

Abstract
The rapidly growing corpus of digital audio material requires novel retrieval strategies for exploring large music collections. Traditional retrieval strategies rely on metadata that describe the actual audio content in words. In the case that such textual descriptions are not available, one requires content-based retrieval strategies which only utilize the raw audio material. In this contribution, we discuss content-based retrieval strategies that follow the query-by-example paradigm: given an audio query, the task is to retrieve all documents that are somehow similar or related to the query from a music collection. Such strategies can be loosely classified according to their "specificity", which refers to the degree of similarity between the query and the database documents. Here, high specificity refers to a strict notion of similarity, whereas low specificity to a rather vague one. Furthermore, we introduce a second classification principle based on "granularity", where one distinguishes between fragment-level and document-level retrieval. Using a classification scheme based on specificity and granularity, we identify various classes of retrieval scenarios, which comprise "audio identification", "audio matching", and "version identification". For these three important classes, we give an overview of representative state-of-the-art approaches, which also illustrate the sometimes subtle but crucial differences between the retrieval scenarios. Finally, we give an outlook on a user-oriented retrieval system, which combines the various retrieval strategies in a unified framework.
Cite as

Peter Grosche, Meinard Müller, and Joan Serrà. Audio Content-Based Music Retrieval. In Multimodal Music Processing. Dagstuhl Follow-Ups, Volume 3, pp. 157-174, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2012)

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@InCollection{grosche_et_al:DFU.Vol3.11041.157,
  author =	{Grosche, Peter and M\"{u}ller, Meinard and Serr\`{a}, Joan},
  title =	{{Audio Content-Based Music Retrieval}},
  booktitle =	{Multimodal Music Processing},
  pages =	{157--174},
  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.Vol3.11041.157},
  URN =		{urn:nbn:de:0030-drops-34711},
  doi =		{10.4230/DFU.Vol3.11041.157},
  annote =	{Keywords: music retrieval, content-based, query-by-example, audio identification, audio matching, cover song identification}
}
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