Auditory representations for long lasting sounds: Insights from neural oscillations and event-related brain potentials

Numerous studies revealed that the sound’s basic features like its frequency and intensity including their temporal dynamics are integrated in a unitary representation. That research focused on short, discrete sounds and mainly disregarded how our brain processes long lasting sounds. We review research utilizing the Mismatch Negativity (MMN) event-related potential and neural oscillatory activity for studying representations for long lasting simple sounds such as sinusoidal tones and complex sounds like speech. We report evidence for a critical temporal constraint for the formation of adequate representations for sounds lasting >350 ms. However, we present research showing that the time-variant characteristics (auditory edges) within long lasting sounds exceeding 350 ms enables the formation of auditory representations. We argue that each edge may open an integration window for a sound representation and that the representations established in adjacent temporal windows of integration can be concatenated into an auditory representation of a long sound.

A novel algorithm for searching frequent gradual patterns from an ordered data set

Mining frequent simultaneous attribute co-variations in numerical databases is also called frequent gradual pattern problem. Few efficient algorithms for automatically extracting such patterns have been reported in the literature. Their main difference resides in the variation semantics used. However in applications with temporal order relations, those algorithms fail to generate correct frequent gradual patterns as they do not take this temporal constraint into account in the mining process. In this paper, we propose an approach for extracting frequent gradual patterns for which the ordering of supporting objects matches the temporal order. This approach considerably reduces the number of gradual patterns within an ordered data set. The experimental results show the benefits of our approach.

Diagnosis of Discrete Event Systems under Temporal Constraints Using Neural Network

The good functioning of a discrete event system is related to how much the temporal constraints are respected. This paper gives a new approach, based on a statistical model and neural network, that allows the verification of temporal constraints in DES. We will perform an online temporal constraint checking which can detect in real time any abnormal functioning related to the violation of a temporal constraint. In the first phase, the construction of temporal constraints from statistical model is shown and after that neural networks are involved in dealing with the online temporal constraint checking.