Audio fingerprinting to identify multiple videos of an event

Cotton, Courtenay V.; Ellis, Daniel P. W.

doi:10.1109/icassp.2010.5496185

Cited by 35 publications

(45 citation statements)

References 8 publications

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“…In [17] the Spatio Temporal Interest Point (STIP) detector was proposed to detect actions in video. In multimodal approaches the audio signal is usually characterized with its Mel Frequency Cepstral Coefficients (MFCC) although more sophisticated techniques can also be used [6]. In our experiment we have only used combinations of SIFT and MFCC features.…”

Section: Related Workmentioning

confidence: 99%

An User-Driven Tool for Interactive Retrieval of Non Annotated Videos

Mendoza

Arnau

Gràcia

et al. 2013

Multimodal Interaction in Image and Video Applications

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Abstract. A prototype to retrieve videos from non-annotated video databases is proposed. We focus on the problem of retrieving relevant videos from the audiovisual signal when the query is unknown for the system, since it is assumed that most of the available annotations are useless, as it is the case for most of the videos from common users in Internet. The approach presented is defined inside of the on-line learning paradigm where user and system collaborate to improve alternative rankings of the items dataset. The user guides the system in the semantic level and the system tries to adapt the low-level similarity distance between items according to the user preferences. The user interacts with the system until a prefixed number of relevant items is retrieved. The video database is represented as a dense graph where a semi-supervised algorithm is used to propagate the user feeedback.

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Section: Related Workmentioning

confidence: 99%

An User-Driven Tool for Interactive Retrieval of Non Annotated Videos

Mendoza

Arnau

Gràcia

et al. 2013

Multimodal Interaction in Image and Video Applications

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“…fingerprinting strategy of [4]. Most recently, Cotton and Ellis [5] explicitly discuss audio fingerprinting for event identification, but do not address synchronization. For a general review of audio fingerprinting methods (not applied to video synchronization), please also see [6].…”

Section: Introductionmentioning

confidence: 99%

Clustering and synchronizing multi-camera video via landmark cross-correlation

Bryan

Smaragdis

Mysore

2012

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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We propose a method to both identify and synchronize multicamera video recordings within a large collection of video and/or audio files. Landmark-based audio fingerprinting is used to match multiple recordings of the same event together and time-synchronize each file within the groups. Compared to prior work, we offer improvements towards event identification and a new synchronization refinement method that resolves inconsistent estimates and allows non-overlapping content to be synchronized within larger groups of recordings. Furthermore, the audio fingerprinting-based synchronization is shown to be equivalent to an efficient and scalable time-difference-of-arrival method using cross-correlation performed on a non-linearly transformed signal.

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“…However, these methods are only applicable when visual features are visible in both videos. Consequently, audio synchronization is widely used for outdoor motion capture [9], mash-ups, identifying video of the same event [10], and is available in commercial editing applications [11]. Synchronizing content captured of outdoor events on consumer devices is particularly challenging given that the microphones may be far apart or disjoint in time, and hence only partially share audio environments.…”

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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Audio fingerprinting to identify multiple videos of an event

Cited by 35 publications

References 8 publications

An User-Driven Tool for Interactive Retrieval of Non Annotated Videos

An User-Driven Tool for Interactive Retrieval of Non Annotated Videos

Clustering and synchronizing multi-camera video via landmark cross-correlation

Temporal synchronization of multiple audio signals

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