Tempo estimation is a fundamental problem in music information retrieval. Most approaches attempt to solve two problems: first finding a dominant pulse and second correcting the metrical level of this pulse. The latter has also been dubbed fixing the octave error. We propose an algorithm for tempo estimation that addresses both problems mostly independently. While using a standard pulse detection technique, for octave error correction, we exploit a simple relationship between a single global feature, average spectral novelty, and listener perception of musical tempo. The proposed method is extremely simple. Nevertheless, it outperforms most existing tempo estimation methods and is on par with the best-performing ones. It thus exemplifies that a global feature-based approach can significantly improve tempo estimation.