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, us-ing 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 detec-tion function (spectral flux) with specific production mod-els, in a simple manner. We propose a hierarchical prun-ing method for an approximate decoding of this statistical model. This strategy reduces the search space in an adap-tive 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 rhyth-mic information is not used for the alignment. 1