One of the most difficult things in practicing musical instruments is improving timbre. Unlike pitch and rhythm, timbre is a high-dimensional and sensuous concept, and learners cannot evaluate their timbre by themselves. To efficiently improve their timbre control, learners generally need a teacher to provide feedback about timbre. However, hiring teachers is often expensive and sometimes difficult. Our goal is to develop a low-cost learning system that substitutes the teacher. We found that a variational autoencoder (VAE), which is an unsupervised neural network model, provides a 2-dimensional user-friendly mapping of timbre. Our system, SonoSpace, maps the learner's timbre into a 2D latent space extracted from an advanced player's performance. Seeing this 2D latent space, the learner can visually grasp the relative distance between their timbre and that of the advanced player. Although our system was evaluated mainly with an alto saxophone, SonoSpace could also be applied to other instruments, such as trumpets, flutes, and drums.