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Publications (7 of 7) Show all publications
Schüldt, C. & Händel, P. (2015). Noise robust integration for blind and non-blind reverberation time estimation. In: Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on: . Paper presented at IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brisbane, Australia, April 19-24, 2015 (pp. 56-60). IEEE Signal Processing Society
Open this publication in new window or tab >>Noise robust integration for blind and non-blind reverberation time estimation
2015 (English)In: Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on, IEEE Signal Processing Society, 2015, p. 56-60Conference paper, Published paper (Refereed)
Abstract [en]

The estimation of the decay rate of a signal section is an integral component of both blind and non-blind reverberation time estimation methods. Several decay rate estimators have previously been proposed, based on, e.g., linear regression and maximum-likelihood estimation. Unfortunately, most approaches are sensitive to background noise, and/or are fairly demanding in terms of computational complexity. This paper presents a low complexity decay rate estimator, robust to stationary noise, for reverberation time estimation. Simulations using artificial signals, and experiments with speech in ventilation noise, demonstrate the performance and noise robustness of the proposed method.

Place, publisher, year, edition, pages
IEEE Signal Processing Society, 2015
Keywords
Reverberation time estimation, blind estimation, decay rate estimation, backward integration
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-165044 (URN)10.1109/ICASSP.2015.7177931 (DOI)000368452400012 ()2-s2.0-84946023741 (Scopus ID)
Conference
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brisbane, Australia, April 19-24, 2015
Note

QC 20150811. QC 20160216

Available from: 2015-04-22 Created: 2015-04-22 Last updated: 2016-02-16Bibliographically approved
Schüldt, C. & Händel, P. (2015). On Implications of the ISO 3382 Backward Integration Method for Automated Decay Rate Estimation. Journal of The Audio Engineering Society, 63(3), 161-173
Open this publication in new window or tab >>On Implications of the ISO 3382 Backward Integration Method for Automated Decay Rate Estimation
2015 (English)In: Journal of The Audio Engineering Society, ISSN 0004-7554, Vol. 63, no 3, p. 161-173Article in journal (Refereed) Published
Abstract [en]

The Schröder backward integration method for estimating the reverberation time of an enclosure, as suggested in the ISO 3382 standard, is analyzed from an estimation theoretic perspective, in a general context that is applicable to both blind and non-blind estimation. Expressions for the estimation bias and variance of the reverberation decay rate are derived and verified using Monte-Carlo simulations. Comparison is made with a straight-forward linear regression method (not using backward integration). It is shown that, even though significantly reducing the estimation variance, the use of backward integration can in many cases mitigate the estimation accuracy due to large bias. This clearly indicates that prudence is called for when using backward integration for automated decay rate estimation problems.

National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-162059 (URN)000351323000003 ()2-s2.0-84925454996 (Scopus ID)
Note

QC 20150323

Available from: 2015-03-20 Created: 2015-03-20 Last updated: 2017-12-04Bibliographically approved
Schüldt, C. & Händel, P. (2014). Decay Rate Estimators and Their Performance for Blind Reverberation Time Estimation. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(8), 1274-1284
Open this publication in new window or tab >>Decay Rate Estimators and Their Performance for Blind Reverberation Time Estimation
2014 (English)In: IEEE/ACM Transactions on Audio, Speech, and Language Processing, ISSN 2329-9290, Vol. 22, no 8, p. 1274-1284Article in journal (Refereed) Published
Abstract [en]

Several approaches for blind estimation of reverberation time have been presented in the literature and decay rate estimation is an integral part of many, if not all, of such approaches. This paper provides both an analytical and experimental comparison, in terms of the bias and variance of three common decay rate estimators; a straight-forward linear regression approach as well as two maximum-likelihood based methods. Situations with and without interfering additive noise are considered. It is shown that the linear regression based approach is unbiased if no smoothing is applied, and that the estimation variance in the absence of noise is constantly about twice that of the maximum-likelihood based methods. It is shown that the methods that do not take possible noise into account suffer from similar estimation bias in the presence of noise. Further, a hybrid method, combining the noise robustness and low computational complexity advantages of the two different maximum-likelihood based methods, is presented.

Keywords
Blind estimation, reverberation time, decay rate, maximum-likelihood estimation
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-147341 (URN)10.1109/TASLP.2014.2328174 (DOI)000340036300005 ()2-s2.0-84911480873 (Scopus ID)
Note

QC 20140630

Available from: 2014-06-26 Created: 2014-06-26 Last updated: 2014-09-12Bibliographically approved
Schüldt, C. & Händel, P. (2013). Blind low-complexity estimation of reverberation time. In: 2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA): . Paper presented at 2013 14th IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2013; New Paltz, NY; United States; 20 October 2013 through 23 October 2013 (pp. 6701875). IEEE conference proceedings
Open this publication in new window or tab >>Blind low-complexity estimation of reverberation time
2013 (English)In: 2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), IEEE conference proceedings, 2013, p. 6701875-Conference paper, Published paper (Refereed)
Abstract [en]

Real-time blind reverberation time estimation is of interest in speech enhancement techniques such as e.g. dereverberation and microphone beamforming. Advances in this field have been made where the diffusive reverberation tail is modeled and the decay rate is estimated using a maximum-likelihood approach. Various methods for reducing the computational complexity have also been presented. This paper proposes a method for even further computational complexity reduction, by more than 60% in some cases, and it is shown through simulations that the results of the proposed method are very similar to that of the original.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2013
Keywords
blind estimation, low-complexity, maximum-likelihood, Reverberation time estimation
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-136728 (URN)10.1109/WASPAA.2013.6701875 (DOI)000349479800067 ()2-s2.0-84893544750 (Scopus ID)978-147990972-8 (ISBN)
Conference
2013 14th IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2013; New Paltz, NY; United States; 20 October 2013 through 23 October 2013
Note

QC 20131219

Available from: 2013-12-07 Created: 2013-12-07 Last updated: 2015-12-07Bibliographically approved
Nilsson, J.-O., Schüldt, C. & Händel, P. (2013). Voice radio communication, pedestrian localization, and the tactical use of 3D audio. In: 2013 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2013: . Paper presented at 2013 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 28-31 October, Montbéliard-Belfort, France, 2013 (pp. 6817918). IEEE Computer Society
Open this publication in new window or tab >>Voice radio communication, pedestrian localization, and the tactical use of 3D audio
2013 (English)In: 2013 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2013, IEEE Computer Society, 2013, p. 6817918-Conference paper, Published paper (Refereed)
Abstract [en]

The relation between voice radio communication and pedestrian localization is studied. 3D audio is identified as a linking technology which brings strong mutual benefits. Voice communication rendered with 3D audio provides a potential low secondary task interference user interface to the localization information. Vice versa, location information in the 3D audio provides spatial cues in the voice communication, improving speech intelligibility. An experimental setup with voice radio communication, cooperative pedestrian localization, and 3D audio is presented and we discuss high level tactical possibilities that the 3D audio brings. Finally, results of an initial experiment, demonstrating the effectiveness of the setup, are presented.

Place, publisher, year, edition, pages
IEEE Computer Society, 2013
Series
International Conference on Indoor Positioning and Indoor Navigation, ISSN 2162-7347
Keywords
Perception, Tracking
National Category
Signal Processing
Identifiers
urn:nbn:se:kth:diva-133472 (URN)10.1109/IPIN.2013.6817918 (DOI)000341663400079 ()2-s2.0-84902133556 (Scopus ID)978-1-4799-4043-1 (ISBN)
Conference
2013 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 28-31 October, Montbéliard-Belfort, France, 2013
Note

QC 20131105

Available from: 2013-11-05 Created: 2013-11-05 Last updated: 2014-10-21Bibliographically approved
Laptev, I., Caputo, B., Schüldt, C. & Lindeberg, T. (2007). Local velocity-adapted motion events for spatio-temporal recognition. Computer Vision and Image Understanding, 108(3), 207-229
Open this publication in new window or tab >>Local velocity-adapted motion events for spatio-temporal recognition
2007 (English)In: Computer Vision and Image Understanding, ISSN 1077-3142, E-ISSN 1090-235X, Vol. 108, no 3, p. 207-229Article in journal (Refereed) Published
Abstract [en]

In this paper, we address the problem of motion recognition using event-based local motion representations. We assume that similar patterns of motion contain similar events with consistent motion across image sequences. Using this assumption, we formulate the problem of motion recognition as a matching of corresponding events in image sequences. To enable the matching, we present and evaluate a set of motion descriptors that exploit the spatial and the temporal coherence of motion measurements between corresponding events in image sequences. As the motion measurements may depend on the relative motion of the camera, we also present a mechanism for local velocity adaptation of events and evaluate its influence when recognizing image sequences subjected to different camera motions. When recognizing motion patterns, we compare the performance of a nearest neighbor (NN) classifier with the performance of a support vector machine (SVM). We also compare event-based motion representations to motion representations in terms of global histograms. A systematic experimental evaluation on a large video database with human actions demonstrates that (i) local spatio-temporal image descriptors can be defined to carry important information of space-time events for subsequent recognition, and that (ii) local velocity adaptation is an important mechanism in situations when the relative motion between the camera and the interesting events in the scene is unknown. The particular advantage of event-based representations and velocity adaptation is further emphasized when recognizing human actions in unconstrained scenes with complex and non-stationary backgrounds.

Place, publisher, year, edition, pages
Elsevier, 2007
Keywords
motion, local features, motion descriptors, matching, velocity adaptation, action recognition, learning, SVM, human movement, representation, scale
National Category
Computer Vision and Robotics (Autonomous Systems) Computer Sciences Mathematics
Identifiers
urn:nbn:se:kth:diva-17110 (URN)10.1016/j.cviu.2006.11.023 (DOI)000250942900002 ()2-s2.0-35548930762 (Scopus ID)
Funder
Swedish Research Council
Note

QC 20100525 QC 20111115

Available from: 2013-04-22 Created: 2010-08-05 Last updated: 2018-01-12Bibliographically approved
Schüldt, C., Laptev, I. & Caputo, B. (2004). Recognizing human actions: A local SVM approach. In: Kittler, J; Petrou, M; Nixon, M (Ed.), PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 3. Paper presented at 17th International Conference on Pattern Recognition (ICPR) Location: British Machine Vis Assoc, Cambridge, ENGLAND Date: AUG 23-26, 2004 (pp. 32-36).
Open this publication in new window or tab >>Recognizing human actions: A local SVM approach
2004 (English)In: PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 3 / [ed] Kittler, J; Petrou, M; Nixon, M, 2004, p. 32-36Conference paper, Published paper (Refereed)
Abstract [en]

Local space-time features capture local events in video and can be adapted to the size, the frequency and the velocity of moving patterns. In this paper we demonstrate how such features can be used for recognizing complex motion patterns. We construct video representations in terms of local space-time features and integrate such representations with SVM classification schemes for recognition. For the purpose of evaluation we introduce a new video database containing 2391 sequences of six human actions performed by 25 people in four different scenarios. The presented results of action recognition justify the proposed method and demonstrate its advantage compared to other relative approaches for action recognition.

Series
INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, ISSN 1051-4651
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:kth:diva-44315 (URN)10.1109/ICPR.2004.1334462 (DOI)000223879500009 ()2-s2.0-10044233701 (Scopus ID)0-7695-2128-2 (ISBN)
Conference
17th International Conference on Pattern Recognition (ICPR) Location: British Machine Vis Assoc, Cambridge, ENGLAND Date: AUG 23-26, 2004
Note
QC 20111025Available from: 2011-10-25 Created: 2011-10-20 Last updated: 2018-01-12Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-3439-0468

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