IRIM at TRECVID2009: High Level Feature Extraction

International audienceThe IRIM group is a consortium of French teams working on Multimedia Indexing and Retrieval. This paper describes our participation to the TRECVID 2009 High Level Features detection task. We evaluated a large number of different descriptors (on TRECVID 2008 data) and tried different fusion strategies, in particular hierarchical fusion and genetic fusion. The best IRIM run has a Mean Inferred Average Precision of 0.1220, which is significantly above TRECVID 2009 HLF detection task median performance. We found that fusion of the classification scores from different classifier types improves the performance and that even with a quite low individual performance, audio descriptors can help

The importance of the french Atlantic mudflats for waders migration

Dans le cadre d'une opération coordonnée au plan international pour suivre la migration pré-multiple des limicoles côtiers depuis leur zone d'hivernage jusqu'à leur lieu de nidification, présentation des résultats concernant les bécasseaux maubèches et variable

Importance du littoral Centre-Ouest Atlantique pour la migration des limicoles côtiers

Counts of migrating waders were carried out during Spring migration, in 1985 and 1986, along the French Atlantic coast, between the rivers Loire and Gironde. Some birds were also caught for biometric measurements, before being banded and released. The importance of the French Atlantic mudflats for Dunlins and Knots wintering in Africa is made clear. This «stopover area» can be considered as an emergency «refueling station» for Knots flying from Western Africa (Mauretania) to the Wadden See. On the basis of our weekly counts, and of the average duration of their stopover, the number of Knots «refueling» on our mudflats was estimated at 100 000 in 1985, and at 35 000 in 1986. The importance of efficiently protecting these mudflats is emphasized.Bredin Denis, Doumeret Alain. Importance du littoral Centre-Ouest Atlantique pour la migration des limicoles côtiers . In: Revue d'Écologie. Supplément n°4, 1987. pp. 221-229

Low-latency speaker spotting with online diarization and detection

This paper introduces a new task termed low-latency speaker spotting (LLSS). Related to security and intelligence applications, the task involves the detection, as soon as possible, of known speakers within multi-speaker audio streams. The paper describes differences to the established fields of speaker diarization and automatic speaker verification and proposes a new protocol and metrics to support exploration of LLSS. These can be used together with an existing, publicly available database to assess the performance of LLSS solutions also proposed in the paper. They combine online diarization and speaker detection systems. Diarization systems include a naive, over-segmentation approach and fully-fledged online diarization using segmental i-vectors. Speaker detection is performed using Gaussian mixture models, i-vectors or neural speaker embeddings. Metrics reflect different approaches to characterise latency in addition to detection performance. The relative performance of each solution is dependent on latency. When higher latency is admissible, i-vector solutions perform well; embeddings excel when latency must be kept to a minimum. With a need to improve the reliability of online diarization and detection, the proposed LLSS framework provides a vehicle to fuel future research in both areas. In this respect, we embrace a reproducible research policy; results can be readily reproduced using publicly available resources and open source codes

Low-latency speaker spotting with online diarization and detection

International audienceThis paper introduces a new task termed low-latency speaker spotting (LLSS). Related to security and intelligence applications, the task involves the detection, as soon as possible, of known speakers within multi-speaker audio streams. The paper describes differences to the established fields of speaker diarization and automatic speaker verification and proposes a new protocol and metrics to support exploration of LLSS. These can be used together with an existing, publicly available database to assess the performance of LLSS solutions also proposed in the paper. They combine online diarization and speaker detection systems. Diarization systems include a naive, over-segmentation approach and fully-fledged online diarization using segmental i-vectors. Speaker detection is performed using Gaussian mixture models, i-vectors or neural speaker embeddings. Metrics reflect different approaches to characterise latency in addition to detection performance. The relative performance of each solution is dependent on latency. When higher latency is admissible, i-vector solutions perform well; embeddings excel when latency must be kept to a minimum. With a need to improve the reliability of online diarization and detection, the proposed LLSS framework provides a vehicle to fuel future research in both areas. In this respect, we embrace a reproducible research policy; results can be readily reproduced using publicly available resources and open source codes

Rapport du groupe de travail sur les oiseaux marins: bilans et propositions de recherches

CNRS RP 185 (2033) / INIST-CNRS - Institut de l'Information Scientifique et TechniqueSIGLEFRFranc

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

Abstract: Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers