This paper considers the auditory attention detection (AAD) paradigm, where the goal is to determine which of two simultaneous speakers a person is attending to. The paradigm relies on recordings of the listener's brain activity, e.g., from electroencephalography (EEG). To perform AAD, decoded EEG signals are typically correlated with the temporal envelopes of the speech signals of the separate speakers. In this paper, we study how the inclusion of various degrees of auditory modelling in this speech envelope extraction process affects the AAD performance, where the best performance is found for an auditory-inspired linear filter bank followed by power law compression. These two modelling stages are computationally cheap, which is important for implementation in wearable devices, such as future neuro-steered auditory prostheses. We also introduce a more natural way to combine recordings (over trials and subjects) to train the decoder, which reduces the dependence of the algorithm on regularization parameters. Finally, we investigate the simultaneous design of the EEG decoder and the audio subband envelope recombination weights vector using either a norm-constrained least squares or a canonical correlation analysis, but conclude that this increases computational complexity without improving AAD performance.

This work shows the importance of using realistic binaural listening conditions and training on a balanced set of experimental conditions to obtain results that are more representative for the true AAD performance in practical applications.

A method is presented to test fibres in tension using direct strain measurement. This eliminates the need to test the fibres at multiple gauge lengths to correct for machine compliance, reducing the number of samples. Additionally, fibre slippage can contribute to the underestimation of the stiffness since this is not considered in the correction procedure. Steel fibres with a diameter of 30 µm, and a known stiffness of 193 GPa, were tested in tension using indirect methods and the direct strain method. Direct strain measurement resulted in a stiffness of 187 ± 12 GPa while the lowest and highest stiffness obtained by the indirect methods are 140 ±2 GPa and 150 ± 4 GPa.The underestimation by the indirect measurement strain methods show the need for a new

Recent research has shown that it is possible to detect which of two simultaneous speakers a person is attending to, using brain recordings and the temporal envelope of the separate speech signals. However, a wide range of possible methods for extracting this speech envelope exists. This paper assesses the effect of different envelope extraction methods with varying degrees of auditory modelling on the performance of auditory attention detection (AAD), and more specifically on the detection accuracy. It is found that sub-band envelope extraction with proper power-law compression yields best performance, and that the use of several more detailed auditory models does not yield a further improvement in performance.

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Abstract-In pay-TV, a service provider offers TV programs and channels to users. To ensure that only authorized users gain access, conditional access systems (CAS) have been proposed. In existing CAS, users disclose to the service provider the TV programs and channels they purchase. We propose a pay-per-view and a pay-per-channel CAS that protect users' privacy. Our pay-per-view CAS employs priced oblivious transfer (POT) to allow a user to purchase TV programs without disclosing which programs were bought to the service provider. In our pay-per-channel CAS, POT is employed together with broadcast attribute-based encryption (BABE) to achieve low storage overhead, collusion resistance, efficient revocation and broadcast efficiency. We propose a new POT scheme and show its feasibility by implementing and testing our CAS on a representative mobile platform.

In a multi-speaker scenario, a major challenge for noise suppression systems in hearing instruments is to determine which sound source the listener is attending to. It has been shown that a linear decoder can extract a neural signal from EEG recordings that is better correlated with the envelope of the attended speech signal than with the envelopes of the other signals. This can be exploited to perform auditory attention detection (AAD), which can then steer a noise suppression algorithm. The speech signal is passed through a model of the auditory periphery before extracting its envelope. We compared 7 different periphery models and found that best AAD performance was obtained with a gamma-tone filter bank followed by power-law compression. Most AAD studies so far have employed a dichotic paradigm, wherein each ear receives a separate speech stream. We compared this to a more realistic setup where speech was simulated to originate form two different spatial locations, and found that although listening conditions were harder, AAD performance was better than for the dichotic setup. Finally, we designed a neuro-steered denoising algorithm that informs the voice activity detection stage of a multi-channel Wiener filter based on AAD, and found a large signal-to-noise-ratio improvement at the output.