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The Speed Submission to DIHARD II: Contributions & Lessons Learned

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Performing speaker diarization while uniquely identifying the speakers in a collection of audio recordings is a challenging task. Based on our previous work on speaker diarization and linking, we developed a system for diarizing longitudinal TV show data s ...

IEEE2016

Non-negative matrix factorization (NMF) based sound source separation involves two phases: First, the signal spectrum is decomposed into components which, in a second step, are clustered in order to obtain estimates of the source signal spectra. The major ...

Ieee2015

Complementary Countermeasures for Detecting Scenic Face Spoofing Attacks

Sébastien Marcel, André Anjos

The face recognition community has finally started paying more attention to the long-neglected problem of spoofing attacks. The number of countermeasures is gradually increasing and fairly good results have been reported on the publicly available databases ...

2013

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Relationships between entities in datasets are often of multiple types, which can naturally be modeled by a multi-layer graph; a common vertex set represents the entities and the edges on different layers capture different types of relationships between th ...

2013

Finding Audio-Visual Events in Informal Social Gatherings

In this paper we address the problem of detecting and localizing objects that can be both seen and heard, e.g., people. This may be solved within the framework of data clustering. We propose a new multimodal clustering algorithm based on a Gaussian mixture ...

2011

Tuning-Robust Initialization Methods for Speaker Diarization

David Imseng

This paper investigates a typical speaker diarization system regarding its robustness against initialization parameter variation and presents a method to reduce manual tuning of these values significantly. The behavior of an agglomerative hierarchical clus ...

2010

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We present an algorithm for clustering sets of detected interest points into groups that correspond to visually distinct structure. Through the use of a suitable colour and texture representation, our clustering method is able to identify keypoints that be ...

2009

Fast high-dimensional Bayesian classification and clustering

Vahid Partovi Nia

We introduce a fast approach to classification and clustering applicable to high-dimensional continuous data, based on Bayesian mixture models for which explicit computations are available. This permits us to treat classification and clustering in a single ...

EPFL2009

A Clustering Method Based on Soft Learning of Model (Prototype) and Dissimilarity Metrics

Arash Arami

Many clustering methods are designed for especial cluster types or have good performance dealing with particular size and shape of clusters. The main problem in this connection is how to define a similarity (or dissimilarity) criterion to make an algorithm ...

Springer-Verlag2009

Learning Cluster Type and Dissimilarity Metric for Each Cluster Using a Set of Possible Cluster Types

Arash Arami

One of the shortcomings of the existing clustering methods is their problems dealing with different shape and size clusters. On the other hand, most of these methods are designed for especial cluster types or have good performance dealing with particular s ...

2007