Abstract-Nowadays, it appears essential to design automatic indexing tools which provide meaningful and efficient means to describe the musical audio content. There is in fact a growing interest for music information retrieval (MIR) applications amongst which the most popular are related to music similarity retrieval, artist identification, musical genre or instrument recognition. Current MIR-related classification systems usually do not take into account the mid-term temporal properties of the signal (over several frames) and lie on the assumption that the observations of the features in different frames are statistically independent. The aim of this paper is to demonstrate the usefulness of the information carried by the evolution of these characteristics over time. To that purpose, we propose a number of methods for early and late temporal integration and provide an in-depth experimental study on their interest for the task of musical instrument recognition on solo musical phrases. In particular, the impact of the time horizon over which the temporal integration is performed will be assessed both for fixed and variable frame length analysis. Also, a number of recently proposed alignment kernels will be used for late temporal integration. For all experiments, the results are compared to a state of the art musical instrument recognition system. Index Terms-Alignment kernels, audio classification, music information retrieval (MIR), musical instrument recognition, support vector machine (SVM), temporal feature integration.

Abstract-In the present work, we introduce the use of Conditional Random Fields (CRFs) for the audio-to-score alignment task. This framework encompasses the statistical models which are used in the literature and allows for more flexible dependency structures. In particular, it allows observation functions to be computed from several analysis frames.Three different CRF models are proposed for our task, for different choices of tradeoff between accuracy and complexity. Three types of features are used, characterizing the local harmony, note attacks and tempo.We also propose a novel hierarchical approach, which takes advantage of the score structure for an approximate decoding of the statistical model. This strategy reduces the complexity, yielding a better overall efficiency than the classic beam search method used in HMM-based models.Experiments run on a large database of classical piano and popular music exhibit very accurate alignments. Indeed, with the best performing system, more than 95% of the note onsets are detected with a precision finer than 100 ms. We additionally show how the proposed framework can be modified in order to be robust to possible structural differences between the score and the musical performance.