Young children retain fast mapped object labels better than shape, color, and texture words

We compared short- and long-term retention of fast mapped color, shape and texture words as well as object labels. In an exposure session, 354 3- and 4-year-old children were shown a set of two familiar and three novel stimuli. One of the novel stimuli was labeled with a new object label, color, shape or texture word. Retention of the mapping between the new word and the novel object or property was measured either five minutes or one week later. After five minutes, retention was significantly above chance in all conditions. However, after one week only the mappings for object labels were retained above chance levels. Our findings suggest that fast mapped object labels are retained long-term better than color, shape and texture words. The results also highlight the importance of comparing short- and long-term retention when studying children’s word learning

Deep Remix: Remixing Musical Mixtures Using a Convolutional Deep Neural Network

Audio source separation is a difficult machine learning problem and performance is measured by comparing extracted signals with the component source signals. However, if separation is motivated by the ultimate goal of re-mixing then complete separation is not necessary and hence separation difficulty and separation quality are dependent on the nature of the re-mix. Here, we use a convolutional deep neural network (DNN), trained to estimate 'ideal' binary masks for separating voice from music, to perform re-mixing of the vocal balance by operating directly on the individual magnitude components of the musical mixture spectrogram. Our results demonstrate that small changes in vocal gain may be applied with very little distortion to the ultimate re-mix. Our method may be useful for re-mixing existing mixes