Gaps, dropouts and short clips of corrupted audio are a common problem and particularly annoying when they occur in speech. This paper uses machine learning to regenerate gaps of up to 320ms in an audio speech signal. Audio regeneration is translated into image regeneration by transforming audio into a Mel-spectrogram and using image in-painting to regenerate the gaps. The full Mel-spectrogram is then transferred back to audio using the Parallel-WaveGAN vocoder and integrated into the audio stream. Using a sample of 1300 spoken audio clips of between 1 and 10 seconds taken from the publicly-available LJSpeech dataset our results show regeneration of audio gaps in close to real time using GANs with a GPU equipped system. As expected, the smaller the gap in the audio, the better the quality of the filled gaps. On a gap of 240ms the average mean opinion score (MOS) for the best performing models was 3.737, on a scale of 1 (worst) to 5 (best) which is sufficient for a human to perceive as close to uninterrupted human speech.
Automated assessment of online spoken materials for use in listening training can be of benefit to language learners. ICALL systems with this facility can help learners sift through collections of online content to find materials that suit their proficiency level and learning goals. Our pilot study assesses the extent to which readability measures can contribute to the listenability assessment of online spoken materials for language learning. Extending these text-based measures to assess listenability is convenient, but their use must be reassessed against spoken materials available online. We compare assessments by four readability measures with expert-assigned assessments of difficulty on online spoken language learning materials. We also evaluate the robustness of these measures against errors arising from automatically generating transcripts and their capability to predict human-based judgments of listenability. Our findings show that these comprehensibility measures can be combined and used to discriminate between materials of different levels of difficulty. We are also able to illustrate a way to conduct a fully automated approach to listenability assessment of spoken language learning materials via text-based measures. Our study also highlights the need for an approach that looks beyond the text, particularly for assessing higher-level materials and spoken materials of different genres.
Current building codes require that new central heating appliances such as oil and gas fired boilers be accompanied by a room thermostat and programmer. The location of the thermostat can lead to problems in operating the central heating system when the temperature local to the thermostat is not consistent with the temperature in rooms where there is demand. Likewise, the interaction with other energy saving measures, such as thermostatic radiator valves (TRV) can produce control issues and lead to negative end user experiences. This paper considers an alternative approach using low cost temperature sensors, open source software and hardware, and interactive dashboard to assist end users in optimizing their energy use with a view to reducing demand and fuel costs.
The potential benefits of cognitive radio networks (CRNs) have urged the wireless community to incorporate them as a solution for potential scarcity and spectrum reutilization. An undersampled autocorrelation-based spectrum sensing scheme that provides orthogonal frequency division multiplexing (OFDM) signal detection in undersampled channels is proposed. The paper introduces a sampling factor that provides several undersampling levels. The resulting undersampled conventional autocorrelation-based (CAB) algorithm is analyzed. The statistical distribution of test statistic is evaluated by employing maximum likelihood estimation (MLE) to extract statistical parameters.
One of the most common tasks performed in VR is navigation through a virtual environment. Real walking, which allows the user to physically move through a physical tracking space as they navigate in a virtual environment, is one of the most natural navigation methods available for VR. However, while virtual worlds can take up vast amounts of space, the physical space the user can walk in is limited. Redirected walking seeks to alleviate this limitation by letting the user walk in a virtual environment that is larger than the physical tracking space available by decoupling the mapping of the user's real and virtual movements. In this paper, we describe an experiment system that evaluates peoples' ability to accurately turn at different levels of rotation gain. We further report on a commissioning experiment that tests the functioning of this research system. Specifically, we asked participants to rotate in two virtual environments, a minimal scene and a visually complex scene, with different levels of induced gain and repeat this task, as a control in the physical world, with their eyes open and then closed. It was found that participants rotated less at higher gain levels, especially in the complex scene.
