Christian Zieger

CLEAR evaluation of acoustic event detection and classification systems

In this paper, we present the results of the Acoustic Event Detection (AED) and Classification (AEC) evaluations carried out in February 2006 by the three participant partners from the CHIL project. The primary evaluation task was AED of the testing portions of the isolated sound databases and seminar recordings produced in CHIL. Additionally, a secondary AEC evaluation task was designed using only the isolated sound databases. The set of meeting-room acoustic event classes and the metrics were agreed by the three partners and ELDA was in charge of the scoring task. In this paper, the various systems for the tasks of AED and AEC and their results are presented.

Classification of Acoustic Maps to Determine Speaker Position and Orientation from a Distributed Microphone Network

Acoustic maps created on the basis of the signals acquired by distributed networks of microphones allow to identify position and orientation of an active talker in an enclosure. In adverse situations of high background noise, high reverberation or unavailability of direct paths to the microphones, localization may fail. This paper proposes a novel approach to talker localization and estimation of head orientation based on the classification of global coherence field (GCF) or oriented GCF maps. Preliminary experiments with data obtained by simulated propagation as well as with data acquired in a real room show that the match with precalculated map models provides a robust behavior in adverse conditions.

Acoustic Based Surveillance System for Intrusion Detection

This paper describes a surveillance system for intrusion detection which is based only on information derived from the processing of audio signals acquired by a Distributed Microphone Network (DMN). In particular the system exploits different acoustic features and estimates of acoustic event positions in order to detect intrusion and reject possible false alarms that may be generated by sound sources inside and outside the monitored room. An evaluation has been conducted in order to measure the performance in terms of false alarms and missed alarms in presence of acoustic events produced inside and outside a test room.The obtained results are very promising and encouraging for future works aimed at improving the actual system accuracy.

A natural acoustic front-end for Interactive TV in the EU-Project DICIT

“Distant-talking Interfaces for Control of Interactive TV” (DICIT) is a European Union-funded project whose main objective is to integrate distant-talking voice interaction as a complementary modality to the use of a remote control in interactive TV systems. Hands-free and seamless control enables a natural user-system interaction providing a suitable means to greatly ease information retrieval. In the given living room scenario the system recognizes commands spoken by multiple and possibly moving users, even in the presence of background noise and TV surround audio. This paper focuses on the multichannel acoustic frontend (MCAF) processing for acoustic scene interpretation which is based on the combination of multi-channel acoustic echo cancellation, blind source separation, beamforming, acoustic event classification, and multiple speaker localization. The fully functional DICIT prototype consists of the MCAF, automatic speech recognition, natural language understanding, mixed-initiative dialogue and satellite connection.

Experiments on distant-talking speaker verification in TV scenario

In this work text-independent speaker verification (SV) in a distant-talking noisy scenario is addressed: users can interact with a TV-system able to understand vocal commands and verify simultaneously the identity of the speaker. The main issues with SV under this scenario are related to reverberation, interfering sound sources (TV output) and usually very short utterances; as a consequence, an increasing confusability among speakers models can be observed. To partially cope with this, we propose a system that exploits the processing of signals acquired by a microphone array and a phonetic class segmentation in unsupervised modality. Comparing the proposed system with a GMM-UBM based system we demonstrate the effectiveness of the approach on data acquired with a real prototype.