Audio-based Sensory Substitution Devices (SSDs) perform adequately when sensing and mapping low-level visual features into sound. Yet, their limitations become apparent when it comes to represent high-level or conceptual information involved in vision. We introduce See ColOr as an SSD that senses color and depth to convert them into musical instrument sounds. In addition and unlike any other approach, our SSD extends beyond a sensing prototype, by integrating computer vision methods to produce reliable knowledge about the physical world (effortlessly for the user). Experiments reported in this thesis reveal that our See ColOr SSD is learnable, functional, and provides easy interaction. In moderate time, participants were able to grasp visual information from the environment out of which they could derive: spatial awareness, ability to find someone, location of daily objects, and skill to walk safely avoiding obstacles. Our encouraging results open a door towards autonomous mobility of the blind.