This paper presents a system for recovering the sectional form of a musical piece: segmentation and labelling of musical parts such as chorus or verse. The system uses three types of acoustic features: mel-frequency cepstral coefficients, chroma, and rhythmogram. An analysed piece is first subdivided into a large amount of potential segments. The distance between each two segments is then calculated and the value is transformed to a probability that the two segments are occurrences of a same musical part. Different features are combined in the probability space and are used to define a fitness measure for a candidate structure description. Musicological knowledge of the temporal dependencies between the parts is integrated into the fitness measure. A novel search algorithm is presented for finding the description that maximises the fitness measure. The system is evaluated with a data set of 557 manually annotated popular music pieces. The results suggest that integrating the musicological model to the fitness measure leads to a more reliable labelling of the parts than performing the labelling as a post-processing step.

In this paper, we describe a system for transcribing polyphonic drum sequences from an acoustic signal to a symbolic representation. Low-level signal analysis is done with an acoustic model consisting of a Gaussian mixture model and a support vector machine. For higher-level modelling, periodic N-grams are proposed to construct a "language model" for music, based on the repetitive nature of musical structure. Also, a technique for estimating relatively long N-grams is introduced. The performance of N-grams in the transcription was evaluated using a database of realistic drum sequences from different genres and yielded a performance increase of 7.6 % compared to a the use of only prior (unigram) probabilities with the acoustic model.

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The structure of a musical piece can be described with segments having a certain time range and a label. Segments having the same label are considered as occurrences of a certain structural part. Here, a system for finding structural descriptions is presented. The problem is formulated in terms of a cost function for structural descriptions. A method for creating multiple candidate descriptions from acoustic input signal is presented, and an efficient algorithm is presented to find the optimal description with regard to the cost function from the candidate set. The analysis system is evaluated with simulations on a database of 50 popular music pieces.

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