An improved hierarchical approach for music-to-symbolic score alignment

We present an efficient approach for an off-line alignment of a symbolic score to a recording of the same piece, using a statistical model. A hidden state model is built from the score, which allows for the use of two different kinds of features, namely chroma vectors and an onset detection function (spectral flux) with specific production models, in a simple manner. We propose a hierarchical pruning method for an approximate decoding of this statistical model. This strategy reduces the search space in an adaptive way, yielding a better overall efficiency than the tested state-of-the art method. Experiments run on a large database of 94 pop songs show that the resulting system obtains higher recognition rates than the dynamic programming algorithm (DTW), with a significantly lower complexity, even though the rhythmic information is not used for the alignment.