This paper presents an algorithm that extracts the tempo of a musical excerpt. The proposed system assumes a constant tempo and deals directly with the audio signal. A sliding window is applied to the signal and two feature classes are extracted. The first class is the log-energy of each band of a mel-scale triangular filterbank, a common feature vector used in various MIR applications. For the second class, a novel feature for the tempo induction task is presented; the strengths of the twelve western musical tones at all octaves are calculated for each audio frame, in a similar fashion with Pitch Class Profile. The time-evolving feature vectors are convolved with a bank of resonators, each resonator corresponding to a target tempo. Then the results of each feature class are com-bined to give the final output. The algorithm was evaluated on the popular ISMIR 2004 Tempo Induction Evaluation Exchange Dataset. Re-sults demonstrate that the superposition of the different types of features enhance the performance of the algo-rithm, which is in the current state-of-the-art algorithms of the tempo induction task.