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In the context of keyword spotting (KWS), the replacement of handcrafted speech features by learnable features has not yielded superior KWS performance. In this study, we demonstrate that filterbank learning outperforms handcrafted speech features for KWS whenever the number of filterbank channels is severely decreased. Reducing the number of channels might yield certain KWS performance drop, but also a substantial energy consumption reduction, which is key when deploying common always-on KWS on low-resource devices. Experimental results on a noisy version of the Google Speech Commands Dataset show that filterbank learning adapts to noise characteristics to provide a higher degree of robustness to noise, especially when dropout is integrated. Thus, switching from typically used 40-channel log-Mel features to 8-channel learned features leads to a relative KWS accuracy loss of only 3.5% while simultaneously achieving a 6.3× energy consumption reduction. 

Abstract Knowledge of quantum mechanical systems is becoming more important for many science and engineering students who are looking to join the emerging quantum workforce. To better prepare a wide range of students for these careers, we must seek to develop new tools to enhance our education in quantum topics. We present initial studies on the use of one of these such tools, Quantum Composer, a 1D quantum simulation and visualization tool developed for education and research purposes. In particular, we conducted five think-aloud interviews with students who worked through an exercise using Quantum Composer that focused on the statics and dynamics of quantum states in a single harmonic well system. Our results show that Quantum Composer helps students to obtain the correct answers to the questions posed, but additional support is needed to facilitate the development of student reasoning behind these answers. We also show that students are able to focus only on the relevant features of Quantum Composer to achieve the task. 

As education researchers gain a broader understanding of how students learn quantum mechanics, new pedagogical and technical resources are being developed to facilitate student learning. To further research-based knowledge of student learning of quantum mechanics, we present a study on the use of Quantum Composer, a flexible, flow-based tool for the exploration and simulation of quantum mechanical systems in one dimension. To explore Composer’s impact on students’ knowledge of quantum mechanics, we carried out think-aloud interviews where students worked through an exercise exploring the statics and time-dynamics of quantum states in single and double harmonic well potentials. Student Outcomes are then cross-coded with their observed Interactions with Composer. We find that defined Outcomes of Recollection, Reinforcement and Discovery happen most often when students are using the Composer tools that allow them to visualize quantum states, simulate their time dynamics, and change parameters repeatedly in order to understand how systems are represented in both the static and dynamic cases.