We present an innovative tempo estimation system that processes acoustic audio signals and does not use any high-level musical knowledge. Our proposal relies on a harmonic + noise decomposition of the audio signal by means of a subspace analysis method. Then, a technique to measure the degree of musical accentuation as a function of time is developed and separately applied to the harmonic and noise parts of the input signal. This is followed by a periodicity estimation block that calculates the salience of musical accents for a large number of potential periods. Next, a multipath dynamic programming searches among all the potential periodicities for the most consistent prospects through time, and finally the most energetic candidate is selected as tempo. Our proposal is validated using a manually annotated test-base containing 961 music signals from various musical genres. In addition, the performance of the algorithm under different configurations is compared. The robustness of the algorithm when processing signals of degraded quality is also measured.