The quality of the speech communication systems, which include noise suppression algorithms, are typically evaluated in laboratory experiments according to the ITU-T Rec. P.835. In this paper, we introduce an open-source implementation of the ITU-T Rec. P.835 for the crowdsourcing approach following the ITU-T Rec. P.808 on crowdsourcing recommendations. The implementation is an extension of the P.808 Toolkit and is highly automated to avoid operational errors. To assess our evaluation method's validity, we compared the Mean Opinion Scores (MOS), calculate using ratings collected with our implementation, and the MOS values from a standard laboratory experiment conducted according to the ITU-T Rec, P.835. Results show a high validity in all three scales (average PCC = 0.961). Results of a round-robin test showed that our implementation is a highly reproducible evaluation method (PCC=1.00). Finally, we investigated the performance of five models deep noise suppression models using our P.835 implementation and show what insights can be learned.
In this paper, we present an update to the NISQA speech quality prediction model that is focused on distortions that occur in communication networks. In contrast to the previous version, the model is trained end-to-end and the time-dependency modelling and time-pooling is achieved through a Self-Attention mechanism. Besides overall speech quality, the model also predicts the four speech quality dimensions Noisiness, Coloration, Discontinuity, and Loudness, and in this way gives more insight into the cause of a quality degradation. Furthermore, new datasets with over 13,000 speech files were created for training and validation of the model. The model was finally tested on a new, live-talking test dataset that contains recordings of real telephone calls. Overall, NISQA was trained and evaluated on 81 datasets from different sources and showed to provide reliable predictions also for unknown speech samples. The code, model weights, and datasets are open-sourced.
In this paper, we introduce a scale for measuring the extrinsic motivation of crowd workers. The new questionnaire is strongly based on the Work Extrinsic Intrinsic Motivation Scale (WEIMS) [17] and theoretically follows the Self-Determination Theory (SDT) of motivation. The questionnaire has been applied and validated in a crowdsourcing micro-task platform. This instrument can be used for studying the dynamics of extrinsic motivation by taking into account individual differences and provide meaningful insights which will help to design a proper incentives framework for each crowd worker that eventually leads to a better performance, an increased well-being, and higher overall quality.
Recently, a new authentication method based on 3D signatures created in air is proposed for mobile devices [4]. The 3D signature is created in air using a properly shaped magnet (a rod or ring) taken in hand. It is based on influencing compass sensor embedded in the new generation of mobile devices. In this paper, we present implementation of this technology on a mobile device (iPhone 3GS). It can demonstrate authentication process using a gesture in the form of a 3D signature freely created in the space around the device by a magnet held in hand. Movement of the magnet in the from of a signature produces a temporal change in the magnetic field sensed by the embedded compass sensor, and can be used as a basis for authentication. As magnetic signatures are performed in 3D space, they can provide a wider choice for authentication, and they can not be easily hardcopied.
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