Timbre

Here I discuss neural networks as one specific type of computer model that learns to process information in a way that may be similar to human perception. Theoretical background for neural networks and an example of training feedforward networks with timbre will be presented. Connectionist vs. Symbolic Models Previous chapters have shown the multi-dimensionality and complexity of auditory signals, and some of the difficulties when it comes to analysis and representation of music from such a sub-symbolic input. But since music perception seems to be a quite “easy” task for humans, we should try to make computer models work in a way similar to the human brain. Therefore I chose to look at artificial neural networks, and how they can be used to simulate neural activity. Before going into more detail about neural networks, it is worth mentioning that such networks are but one of many different models of “intelligent” computational systems. Such models in the world of “artificial intelligence” seem to be divided in two major directions; symbolic and rule-based models on one side, and connectionist models on the other. ...

Here, I focus on how we can analyse, visualize and synthesize sound, or more specifically the timbre of instruments. Pitch and Timbre Perception Our perception of music is based on the grouping of frequencies in time and space. That is why a set of frequencies can be heard as a specific tone with an associated pitch, loudness and timbre. Such grouping is done by relating frequencies that have their origin close in spatial location, have similar onset time, and move in the same direction. The problem, however, is that there are no computational tools that can do this in an immediate and straight forward way like the human brain. ...