DeepVoCoder: A CNN model for compression and coding of narrow band speech

This paper proposes a convolutional neural network (CNN)-based encoder model to compress and code speech signal directly from raw input speech. Although the model can synthesize wideband speech by implicit bandwidth extension, narrowband is preferred for IP telephony and telecommunications purposes. The model takes time domain speech samples as inputs and encodes them using a cascade of convolutional filters in multiple layers, where pooling is applied after some layers to downsample the encoded speech by half. The final bottleneck layer of the CNN encoder provides an abstract and compact representation of the speech signal. In this paper, it is demonstrated that this compact representation is sufficient to reconstruct the original speech signal in high quality using the CNN decoder. This paper also discusses the theoretical background of why and how CNN may be used for end-to-end speech compression and coding. The complexity, delay, memory requirements, and bit rate versus quality are discussed in the experimental results.Web of Science7750897508

Teaching grasping points using natural movements

Trabajo presentado a la 18th Catalan Conference on Artificial Intelligence (CCIA) celebrada en Valencia (España) del 21 al 23 de octubre de 2015.The research on robots performing every-day tasks at home has pursued the problem of the manipulation of everyday objects. Among them, grasping a cloth is a challenging task, as the textile is highly-deformable and it is not straightforward to define a generic grasping point. In this paper, we address this problem by introducing a new robot interaction method that enables unexperienced users to control the robot in a natural way. When the robot proposes a grasping point, the user is able to teach the robot a new one. The data collected using this method is then used for training a system using linear regression method, which produces better grasping points and allowing better manipulation actions. The experiments demonstrates the validity of the new interaction method and its potential to improve the point-grasping selection algorithm.Peer Reviewe