Novel approach for detecting applause in continuous meeting speech

Manoj, C.; Magesh, S.; Sankaran, Aditya Sriram; Manikandan, M. Sabarimalai

doi:10.1109/icectech.2011.5941827

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…Gesture analysis can benefit from improved self-contact signature predictions. For example, applause detection (Manoj et al 2011) can possibly be performed from soundless videos as the detection of frequent self-contact between one's hands. Similarly, a self-contact signature such as covering ears with both hands can be used as a signal of patients' noise-sensitivity, with applications in robot-assisted autism therapy (Rudovic et al 2017;Marinoiu et al 2018).…”

Section: Related Workmentioning

confidence: 99%

Learning Complex 3D Human Self-Contact

Fieraru

Zanfir

Oneata

et al. 2021

AAAI

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Monocular estimation of three dimensional human self-contact is fundamental for detailed scene analysis including body language understanding and behaviour modeling. Existing 3d reconstruction methods do not focus on body regions in self-contact and consequently recover configurations that are either far from each other or self-intersecting, when they should just touch. This leads to perceptually incorrect estimates and limits impact in those very fine-grained analysis domains where detailed 3d models are expected to play an important role. To address such challenges we detect self-contact and design 3d losses to explicitly enforce it. Specifically, we develop a model for Self-Contact Prediction (SCP), that estimates the body surface signature of self-contact, leveraging the localization of self-contact in the image, during both training and inference. We collect two large datasets to support learning and evaluation: (1) HumanSC3D, an accurate 3d motion capture repository containing 1,032 sequences with 5,058 contact events and 1,246,487 ground truth 3d poses synchronized with images collected from multiple views, and (2) FlickrSC3D, a repository of 3,969 images, containing 25,297 surface-to-surface correspondences with annotated image spatial support. We also illustrate how more expressive 3d reconstructions can be recovered under self-contact signature constraints and present monocular detection of face-touch as one of the multiple applications made possible by more accurate self-contact models.

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Section: Related Workmentioning

confidence: 99%

Learning Complex 3D Human Self-Contact

Fieraru

Zanfir

Oneata

et al. 2021

AAAI

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“…where % corresponds to the modulo operation 9 . Chroma filter banks discussed in [22] are primarily designed for Western classical music.…”

Section: Segmentation Of Carnatic Music Concertmentioning

confidence: 99%

“…In figure 11, the filter banks are placed based on the mel scale as defined in Eq. (9). In figure 9, the filter banks are placed based on the cent scale as defined in Eq.…”

Section: 1b Importance Of Cfcc Feature For Carnatic Musicmentioning

confidence: 99%

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Segmentation of continuous audio recordings of Carnatic music concerts into items for archival

Padi

Murthy

2018

Sādhanā

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Concert recordings of Carnatic music are often continuous and unsegmented. At present, these recordings are manually segmented into items for making CDs. The objective of this paper is to develop algorithms that segment continuous concert recordings into items using applause as a cue. Owing to the 'here and now' nature of applauses, the number of applauses exceeds the number of items in the concert. This results in a concert being fragmented into different segments. In the first part of the paper, applause locations are identified using time, and spectral domain features, namely, short-time energy, zero-crossing rate, spectral flux and spectral entropy. In the second part, inter-applause segments are merged if they belong to the same item. The main component of every item in a concert is a composition. A composition is characterised by an ensemble of vocal (or main instrument), violin (optional) and percussion. Inter-applause segments are classified into three segments, namely, vocal solo, violin solo, composition and thaniavarthanam using tonic normalised cent filterbank cepstral coefficients. Adjacent composition segments are merged into a single item, if they belong to the same melody. Meta-data corresponding to the concert in terms of items, available from listeners, are matched to the segmented audio. The applauses are further classified based on strength using Cumulative Sum. The location of the top three highlights of every concert is documented. The performance of the proposed approaches to applause identification, inter-applause classification and mapping of items is evaluated on 50 live recordings of Carnatic music concerts. The applause identification accuracy is 99%, and the inter-and intra-item classification is 93%, while the mapping accuracy is 95%. Keyword. Cent filter-bank cepstral coefficients; segmentation of concerts; applause detection; classification of music segments. The work presented in this paper is an extension and includes extensive analysis on Carnatic music.

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Audio Denoising Using Deep Neural Networks

Mohammed

Radhika²

2022

Intelligent Data Communication Technologies and Internet of Things

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Novel approach for detecting applause in continuous meeting speech

Cited by 7 publications

References 7 publications

Learning Complex 3D Human Self-Contact

Learning Complex 3D Human Self-Contact

Segmentation of continuous audio recordings of Carnatic music concerts into items for archival

Audio Denoising Using Deep Neural Networks

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