This paper introduces a novel way to detect metrical structure in music. We introduce a way to compute autocorrelation such that the distribution of energy in phase space is preserved in a matrix. The resulting autocorrelation phase matrix is useful for several tasks involving metrical structure. First we can use the matrix to enhance standard autocorrelation by calculating the Shannon entropy at each lag. This approach yields improved results for autocorrelationbased tempo induction. Second, we can efficiently search the matrix for combinations of lags that suggest particular metrical hierarchies. This approach yields a good model for predicting the meter of a piece of music. Finally we can use the phase information in the matrix to align a candidate meter with music, making it possible to perform beat induction with an autocorrelation-based model. We present results for several meter prediction and tempo induction datasets, demonstrating that the approach is competitive with models designed specifically for these tasks. We also present preliminary beat induction results on a small set of artificial patterns.
['Douglas Eck', 'Norman Casagrande']
Finding Meter in Music Using an Autocorrelation Phase Matrix and Shannon Entropy