An eight-channel database of head-related impulse responses (HRIRs) and binaural room impulse responses (BRIRs) is introduced. The impulse responses (IRs) were measured with three-channel behind-the-ear (BTEs) hearing aids and an in-ear microphone at both ears of a human head and torso simulator. The database aims at providing a tool for the evaluation of multichannel hearing aid algorithms in hearing aid research. In addition to the HRIRs derived from measurements in an anechoic chamber, sets of BRIRs for multiple, realistic head and sound-source positions in four natural environments reflecting dailylife communication situations with different reverberation times are provided. For comparison, analytically derived IRs for a rigid acoustic sphere were computed at the multichannel microphone positions of the BTEs and differences to real HRIRs were examined. The scenes' natural acoustic background was also recorded in each of the real-world environments for all eight channels. Overall, the present database allows for a realistic construction of simulated sound fields for hearing instrument research and, consequently, for a realistic evaluation of hearing instrument algorithms.

Independent component analysis (ICA) has proven useful for modeling brain and electroencephalographic (EEG) data. Here, we present a new, generalized method to better capture the dynamics of brain signals than previous ICA algorithms. We regard EEG sources as eliciting spatio-temporal activity patterns, corresponding to, e.g. trajectories of activation propagating across cortex. This leads to a model of convolutive signal superposition, in contrast with the commonly used instantaneous mixing model. In the frequency-domain, convolutive mixing is equivalent to multiplicative mixing of complex signal sources within distinct spectral bands. We decompose the recorded spectraldomain signals into independent components by a complex infomax ICA algorithm. First results from a visual attention EEG experiment exhibit: (1) sources of spatio-temporal dynamics in the data, (2) links to subject behavior, (3) sources with a limited spectral extent, and (4) a higher degree of independence compared to sources derived by standard ICA. q

We investigate an XY spin-glass model in which both spins and interactions (or couplings) evolve in time, but with widely separated time-scales. For large times this model can be solved using replica theory, requiring two levels of replicas, one level for the spins and one for the couplings. We define the relevant order parameters, and derive a phase diagram in the replica-symmetric approximation, which exhibits two distinct spin-glass phases. The first phase is characterized by freezing of the spins only, whereas in the second phase both spins and couplings are frozen. A detailed stability analysis leads also to two distinct corresponding de Almeida-Thouless lines, each marking continuous replica-symmetry breaking. Numerical simulations support our theoretical study.

State-of-the-art classifiers like hidden Markov models (HMMs) in combination with mel-frequency cepstral coefficients (MFCCs) are flexible in time but rigid in the spectral dimension. In contrast, part-based models (PBMs) originally proposed in computer vision consist of parts in a fully deformable configuration. The present contribution proposes to employ PBMs in the spectro-temporal domain for detection of emergency siren sounds in traffic noise, resulting in a classifier that is robust to shifts in frequency induced, e.g., by Doppler-shift effects. Two improvements over standard machine learning techniques for PBM estimation are proposed: (i) Spectro-temporal part ("appearance") extraction is initialized by interest point detection instead of random initialization and (ii) a discriminative training approach in addition to standard generative training is implemented. Evaluation with self-recorded police sirens and traffic noise gathered on-line demonstrates that PBMs are successful in acoustic siren detection. One hand-labeled and two machine learned PBMs are compared to standard HMMs employing mel-spectrograms and MFCCs in clean and multi condition (multiple SNR) training settings. Results show that in clean condition training, hand-labeled PBMs and HMMs outperform machine-learned PBMs already for test data with moderate additive noise. In multi condition training, the machine learned PBMs outperform HMMs on most SNRs, achieving high accuracies and being nearly optimal up to 5 dB SNR. Thus, our simulation results show that PBMs are a promising approach for acoustic event detection (AED)