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The perception of one’s own voice influences the acceptance of hearing devices, such as headphones, headsets or hearing aids. When these devices fully or partially occlude the ear canal, the wearer’s own voice sounds boomy or like talking in a barrel. This is called occlusion effect . Occluding the ear canal results in an amplification of body-conducted sounds, mainly at low frequencies, and an attenuation of air-conducted sounds, predominantly at high frequencies, compared to the open ear. 

We present a scalable and efficient neural waveform coding system for speech compression. We formulate the speech coding problem as an autoencoding task, where a convolutional neural network (CNN) performs encoding and decoding as a neural waveform codec (NWC) during its feedforward routine. The proposed NWC also defines quantization and entropy coding as a trainable module, so the coding artifacts and bitrate control are handled during the optimization process.

Visual place recognition is one of the essential and challenging problems in the fields of robotics. In this letter, we for the first time explore the use of multi-modal fusion of semantic and visual modalities in dynamics-invariant space to improve place recognition in dynamic environments. We achieve this by first designing a novel deep learning architecture to generate the static semantic segmentation and recover the static image directly from the corresponding dynamic image.