Karim Helwani

Improving event detection for audio surveillance using Gabor filterbank features

Acoustic event detection in surveillance scenarios is an important but difficult problem. Realistic systems are struggling with noisy recording conditions. In this work, we propose to use Gabor filterbank features to detect target events in different noisy background scenes. These features capture spectro-temporal modulation frequencies in the signal, which makes them suited for the detection of non-stationary sound events. A single-class detector is constructed for each of the different target events. In a hierarchical framework, the separate detectors are combined to a multi-class detector. Experiments are performed using a database of four different target sounds and four background scenarios. On average, the proposed features outperform conventional features in all tested noise levels, in terms of detection and classification performance.

Efficient adaptive filtering in compressive domains for sparse systems and relation to transform-domain adaptive filtering

In this paper we introduce a novel class of efficient multichannel adaptive filtering algorithms for sparse FIR systems. By suitably integrating ideas from compressed sensing and adaptive filter theory, this class of algorithms allows to significantly reduce the actual number of adaptive coefficients in an efficient way. These algorithms, termed compressive-domain adaptive filters, can be interpreted as a novel type of transform-domain techniques. They can also be seen as adaptive approach in an efficiently self-learning manifold based on the prior knowledge of sparseness of the system. An important property of this concept is that it does not place additional restrictions on the input signal characteristics. Based on the well-known RLS algorithm as a reference, the simulation results confirm that the proposed algorithm converges at acceptable rates, even for strongly colored signals such as speech and audio.

Localization of sound sources with known statistics in the presence of interferers

Methods are available for simultaneous localization of multiple (unknown) audio sources using microphone arrays. Typical algorithms aim at localizing all active sources. They moreover require that the number of sources is known and is less than or equal the number of microphones. This constraint cannot be satisfied in many reallife situations and noisy environments. We present an algorithm for localizing an audio source with known statistics in a multi-source environment. The proposed method circumvents the mentioned problems by using a phase-preserving signal extraction method on the input signal. A binary mask is estimated and used to retain only the information of the target source in the original microphone signals. The masked signals are fed to a modified version of a conventional localization algorithm, which now localizes only the target source. Experimental results obtained from real recordings show that the proposed method can successfully detect and localize the target source.

Multichannel adaptive filtering in compressive domains

In this paper, we give a study on reducing the coefficients to be estimated in an adaptive sparse multichannel system identification problem. We present an approach to perform the adaptation in a compressed representation of the sparse system without requiring prior knowledge about the dimensions in which the system has significant components. The presented technique exploits the ability of sparse systems to be compressed offering a reduction of the adaptive filter coefficients in addition to high convergence rates.

Low complexity unsupervised multi-camera color calibration with application to panoramic video capturing

In this study, a novel approach to perform unsupervised color correction on images captured with multiple cameras is presented. The proposed algorithm requires a minimal common reference area for each pair of cameras in the setup, which makes it well suited for high resolution panoramic images. Moreover, due to its simplicity it allows to cope with realtime constraints in an online scenario.