Machine-assisted editing of user-generated content

Cremer, Markus; Cook, Randall

doi:10.1117/12.807515

Cited by 3 publications

(1 citation statement)

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“…For example, Bryan et al use audio-fingerprinting [13] to match strongly correlating signal-pairs [14] that are iteratively merged into larger clusters until a global solution is found. Similarly, Shrestha et al [15] and Cremer et al [16] generate multiple audiofingerprints for small segments in each audio track which are then individually matched against each other. Such bottomup approaches are sensitive to the propagation of bad initial matching decisions, meaning all non-overlapping or poor candidate pairwise matches must be pruned in advance.…”

Section: Introductionmentioning

confidence: 99%

Temporal synchronization of multiple audio signals

Kammerl

Birkbeck

Inguva

et al. 2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Given the proliferation of consumer media recording devices, events often give rise to a large number of recordings. These recordings are taken from different spatial positions and do not have reliable timestamp information. In this paper, we present two robust graph-based approaches for synchronizing multiple audio signals. The graphs are constructed atop the over-determined system resulting from pairwise signal comparison using cross-correlation of audio features. The first approach uses a Minimum Spanning Tree (MST) technique, while the second uses Belief Propagation (BP) to solve the system. Both approaches can provide excellent solutions and robustness to pairwise outliers, however the MST approach is much less complex than BP. In addition, an experimental comparison of audio features-based synchronization shows that spectral flatness outperforms the zero-crossing rate and signal energy.

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Section: Introductionmentioning

confidence: 99%

Temporal synchronization of multiple audio signals

Kammerl

Birkbeck

Inguva

et al. 2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Social focus of attention as a time function derived from multimodal signals

Korchagin

Abutalebi

2011

2011 IEEE International Conference on Multimedia and Expo

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In this paper, we present the results of a study on the social focus of attention as a time function derived from the multisource multimodal signals, recorded by different personal capturing devices during social events. The core of the approach is based on fission and fusion of multichannel audio, video and social modalities to derive the social focus of attention. The results achieved to date on 16+ hours of real-life data prove the feasibility of the approach.

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