A flexible bio-inspired hierarchical model for analyzing musical timbre

A flexible and multipurpose bio-inspired hierarchical model for analyzing musical timbre is presented in this paper. Inspired by findings in the fields of neuroscience, computational neuroscience, and psychoacoustics, not only does the model extract spectral and temporal characteristics of a signal, but it also analyzes amplitude modulations on different timescales. It uses a cochlear filter bank to resolve the spectral components of a sound, lateral inhibition to enhance spectral resolution, and a modulation filter bank to extract the global temporal envelope and roughness of the sound from amplitude modulations. The model was evaluated in three applications. First, it was used to simulate subjective data from two roughness experiments. Second, it was used for musical instrument classification using the k-NN algorithm and a Bayesian network. Third, it was applied to find the features that characterize sounds whose timbres were labeled in an audiovisual experiment. The successful application of the proposed model in these diverse tasks revealed its potential in capturing timbral information.

Improving CEGEP Students’ ESL Accuracy through L1/L2 Synchronous Text-based Screen-Sharing Tasks

Abstract : CEGEPs are now reaping the ‘first fruits’ of the last Educational Reform in Quebec and as a result, ‘English as Second Language’ (ESL) teachers are noticing an improvement in fluency and a seemingly lower level of inhibition when it comes to production skills. However, this output is accompanied by a noticeable lack of accuracy. Keeping in mind that the purpose of language is communication, we need to find a way to reduce the number of basic common errors made by CEGEP ESL students, while maintaining a natural and motivating learning environment. Thanks to recent advances in computer-mediated communication (CMC), we now have the necessary tools to access peer native speakers throughout the world. Although this technology can be used for other language courses, this study explored the potential value of collaboration with native English speakers through the use of synchronous screen-sharing technology, in order to improve CEGEP ESL students’ accuracy in writing. The instrumentation used consisted of a questionnaire, tests, plus documents of collaborative tasks, using the ‘Google for Education’ screen-sharing tool. Fourteen Intermediate/Advanced ESL CEGEP students participated in this study. Despite the positive tendencies revealed, only a prolonged use of the innovative method yielded a significant positive impact. Moreover, a mixed linear regression for the group with more L1 intervention revealed a significant correlation between the number of errors in the task documents and the number of tasks accomplished. Thus, it could be inferred that ESL accuracy improves in proportion to the number of synchronous text-based screen-sharing tasks done with L1 collaboration.