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A framework devised for the storage of metadata describing 3DTV content, derived from the application of several 3DTV media analysis tools such as shot/scene boundary detection, person detection/tracking/recognition, facial expression recognition, music/speech segmentation, speaker diarization and music genre/mood characterization, in an MPEG 7/AVDP compatible manner will be presented in this contribution. AVDP was designed by having mainly single channel videos in mind. Thus, in order to utilize it for the description of stereoscopic video and multichannel audio content, a number of implementation decisions, that cater to the particularities of such content (storage of stereoscopic quality information, relations between entities in the various channels etc) had to be taken and will be presented in this contribution. Examples of using AVDP to describe the results of analysis algorithms on stereo video and multichannel audio content will be presented. Additionally, several Classification Schemes used in the proposed framework will be discussed, since some terms may be useful in other applications. Finally, the contribution will include a discussion on possible extensions/modifications of the MPEG-7 standard or the AVDP profile to better cover the needs of stereoscopic and mutiview content description. The proposed framework was devised within 3DTVS (3DTV Content Search), a European FP7 project that aims at devising 3DTV audiovisual content analysis description, indexing, search and browsing methods and incorporating such functionalities in 3D audio-visual content archives.

cote interne IRCAM: Papachristou14a; None / None; National audience; The Use of MPEG-7 AVDP Profile in 3DTV Audiovisual Content Description

International audience; We present an algorithm for sound analysis and resynthesis with local automatic adaptation of time-frequency resolution. There exists several algorithms allowing to adapt the analysis window depending on its time or frequency location; in what follows we propose a method which select the optimal resolution depending on both time and frequency. We consider an approach that we denote as analysis-weighting, from the point of view of Gabor frame theory. We analyze in particular the case of different adaptive time- varying resolutions within two complementary frequency bands; this is a typical case where perfect signal reconstruction cannot in general be achieved with fast algorithms, causing a certain error to be minimized. We provide examples of adaptive analyses of a music sound, and outline several possibilities that this work opens.

cote interne IRCAM: Liuni11b; None / None; National audience; Change detection within an audio stream is an important task in several domains, such as classification and segmentation of a sound or of a music piece, as well as indexing of broadcast news or surveillance applications. In this paper we propose two novel methods for spectral change detection without any assumption about the input sound: they are both based on the evaluation of information measures applied to a time-frequency representation of the signal, and in particular to the spectrogram. The class of measures we consider, the Rényi entropies, are obtained by extending the Shannon entropy definition: a biasing of the spectrogram coefficients is realized through the dependence of such measures on a parameter, which allows refined results compared to those obtained with standard divergences. These methods provide a low computational cost and are well-suited as a support for higher level analysis, segmentation and classification algorithms.

We propose a method for automatic local time-adaptation of the spectrogram of audio signals: it is based on the decomposition of a signal within a Gabor multi-frame through the STFT operator. The sparsity of the analysis in every individual frame of the multi-frame is evaluated through the R\'enyi entropy measures: the best local resolution is determined minimizing the entropy values. The overall spectrogram of the signal we obtain thus provides local optimal resolution adaptively evolving over time. We give examples of the performance of our algorithm with an instrumental sound and a synthetic one, showing the improvement in spectrogram displaying obtained with an automatic adaptation of the resolution. The analysis operator is invertible, thus leading to a perfect reconstruction of the original signal through the analysis coefficients.

International audience; In this paper we propose a method for automatic local time adaptation of the spectrogram of an audio signal, based on its decomposition within a Gabor multi-frame. The sparsity of the analyses within each individual frame is evaluated through the Rényi entropies measures. According to the sparsity of the decompositions, an optimal resolution and a reduced multi-frame are determined, defining an adapted spectrogram with variable resolution and hop size. The composition of such a reduced multi-frame allows an immediate definition of a dual frame: re-synthesis techniques for this adapted analysis are easily derived by the traditional phase vocoder scheme.