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Cherla, S.; Weyde, T.; Garcez, A.; Pearce, M. (2013)
Publisher: International Society for Music Information Retrieval
Languages: English
Types: Part of book or chapter of book
Subjects: QA75, M
The analysis of sequences is important for extracting information from music owing to its fundamentally temporal nature. In this paper, we present a distributed model based on the Restricted Boltzmann Machine (RBM) for melodic sequences. The model is similar to a previous successful neural network model for natural language [2]. It is first trained to predict the next pitch in a given pitch sequence, and then extended to also make use of information in sequences of note-durations in monophonic melodies on the same task. In doing so, we also propose an efficient way of representing this additional information that takes advantage of the RBM’s structure. In our evaluation, this RBM-based prediction model performs slightly better than previously evaluated n-gram models in most cases. Results on a corpus of chorale and folk melodies showed that it is able to make use of information present in longer contexts more effectively than n-gram models, while scaling linearly in the number of free parameters required.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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