Recognition Approach Sample Clauses

Recognition Approach. The basis of our recognition model is formed by a pre-trained MobileNetV2 (Sandler, 2018) network architecture, trained for object recognition on the comprehensive ImageNet (Xxxx, 2009) database. Since the AffectNet database features not only valence - arousal scores, but also associated categorical emotion labels, we can refine the preceding transfer learning approaches with a multi-task learning strategy. Figure (reference) illustrates the architecture: The pre-trained base model shares layers with two model heads. One model head is learning the valence - arousal recognition task, the second one is trained in classification of eight categorical emotional classes. Both model heads can backward propagate into the shared base model layers. Hereby, the second task (categorical emotions) is used to stabilize and improve performance of the initial learning task (valence/arousal). The second, redundant model head is discarded after training. The base model (further trained by the transfer and multi-task steps) together with the valence - arousal model head forms the conclusive recognition model.

Recognition Approach. As mentioned above, the goal of our online classification system is a continuous and reactive analysis of the audio signal. The processing of raw audio with neural networks can process very short signal 1 xxxx://xxx.xx.xxxx.xx.uk/~pszmv/page5/page8/page8.html segments (i.e. 50 milliseconds) and enables classification at a very high sample rate – resulting in the targeted smooth and reactive assessment of the current arousal level. The base model (SoundNet2) is trained to recognise scenes based on present sounds in the audio channel. Of course, scene classification is not the target of our emotional analysis. However, the output of the last pooling layer of the pre-trained model represents a compact and focused distillation of the audio signal that is well suited for further processing. We feed the output of the last pooling layer (pool 5) of the SoundNet architecture into two additional fully connected layers which are meant to map the representation to the final arousal score. Figure 4: SoundNet (Aytar, 2016) is a deep convolutional network for natural sound recognition. The network is trained by transferring discriminative knowledge from visual recognition networks into sound networks. The approach capitalizes on the synchronization of vision and sound in video.

Recognition Approach. The PRESENT sensing system implements a deep learning based approach by continuously classifying the audio signal with University of Augsburg’s VadNet (Xxxxxx, 2018). Output of the classification model is smoothed over consecutive analysis frames and results in a continuous estimation of NOISE versus VOICE classes. By tracking this assessment, we are able detect the on- and offset of spoken sentences in the audio channel. This information is for example used in dialogue tasks, where spoken sentences are extracted from the raw audio signal and transmitted as dialogue acts to the external dialogue management tool. Here, speech recognition is carried out and corresponding answers are created. Figure 5: The VadNet model has been trained with Tensorflow (Abadi, 2016). It takes as input the raw audio input and feeds it into a 3-layer convolutional network. The result of this filter operation is then processed by a 2-layer Recurrent Network containing 64 RLU cells. The final bit is a fully- connected layer, which applies a softmax and maps the input to a tuple <noise, voice>.