It is only very recently that systems have been developed that transcribe polyphonic music in even limited generality. Two of these systems [Kashino et al. 1995, Martin 1996] have been built within a blackboard framework, integrating front ends based on sinusoidal analysis with musical knowledge. These and other systems to date rely on instrument models for detecting octaves. Recent results have shown that an autocorrelation-based front end may make bottom-up detection of octaves possible, thereby improving system performance as well as reducing the distance between transcription models and human audition. This report outlines the blackboard approach to automatic music transcription and presents a new system based on the log-lag correlogram of [Ellis 1996]. Preliminary results are presented, outlining the bottom-up detection of octaves and transcription of simple polyphonic music.