Prograded Holocene beach-ridges with superimposed dunes in north-east Ireland: mechanisms and timescales of fine and coarse beach sediment decoupling and deposition

Two depositional models to account for Holocene gravel-dominated beach ridges covered by dunes, occurring on the northern coast of Ireland, are considered in the light of infrared-stimulated luminescence ages of sand units within beach ridges, and 14C ages from organic horizons in dunes. A new chronostratigraphy obtained from prograded beach ridges with covering dunes at Murlough, north-east Ireland, supports a model of mesoscale alternating sediment decoupling (ASD) on the upper beach, rather than macroscale sequential sediment sourcing to account for prograded beach ridges and covering dunes. The ASD model specifies storm or fair-weather sand beach ridges forming at high-tide positions (on an annual basis at minimum), which acted as deflationary sources for landward foredune development. Only a limited number of such late-Holocene beach ridges survive in the observed prograded series. Beach ridges only survive when capped by storm-generated gravel beaches that are deposited on a mesoscale time spacing of 50–130 years. The morphodynamic shift from a dissipative beach face for dune formation to a reflective beach face for gravel capping appears to be controlled by the beach sand volume falling to a level where reflective conditions can prevail. Sediment volume entering the beach is thought to have fluctuated as a function of a forced regression associated with the falling sea level from the mid-Holocene highstand (ca. 6000 cal. yr BP) identified in north-east Ireland. The prograded beach ridges dated at ca. 3000 to 2000 cal. yr BP indicate that the Holocene highstand’s regressive phase may have lasted longer than previously specified.

Speech Recognition with unknown partial feature corruption - a review of the union model

This paper provides a summary of our studies on robust speech recognition based on a new statistical approach – the probabilistic union model. We consider speech recognition given that part of the acoustic features may be corrupted by noise. The union model is a method for basing the recognition on the clean part of the features, thereby reducing the effect of the noise on recognition. To this end, the union model is similar to the missing feature method. However, the two methods achieve this end through different routes. The missing feature method usually requires the identity of the noisy data for noise removal, while the union model combines the local features based on the union of random events, to reduce the dependence of the model on information about the noise. We previously investigated the applications of the union model to speech recognition involving unknown partial corruption in frequency band, in time duration, and in feature streams. Additionally, a combination of the union model with conventional noise-reduction techniques was studied, as a means of dealing with a mixture of known or trainable noise and unknown unexpected noise. In this paper, a unified review, in the context of dealing with unknown partial feature corruption, is provided into each of these applications, giving the appropriate theory and implementation algorithms, along with an experimental evaluation.