A Topic Model for Melodic Sequences
This addresses the challenge of modeling melodic sequences for music analysis, though it appears incremental as it builds on existing topic modeling approaches.
The authors tackled the problem of learning a probabilistic model for melody from musical sequences in the same genre, introducing the Variable-gram Topic Model that couples latent topics with contextual information. They showed it achieved the highest performance in next-step prediction and distribution similarity compared to existing models like LDA and Topic Bigram.
We examine the problem of learning a probabilistic model for melody directly from musical sequences belonging to the same genre. This is a challenging task as one needs to capture not only the rich temporal structure evident in music, but also the complex statistical dependencies among different music components. To address this problem we introduce the Variable-gram Topic Model, which couples the latent topic formalism with a systematic model for contextual information. We evaluate the model on next-step prediction. Additionally, we present a novel way of model evaluation, where we directly compare model samples with data sequences using the Maximum Mean Discrepancy of string kernels, to assess how close is the model distribution to the data distribution. We show that the model has the highest performance under both evaluation measures when compared to LDA, the Topic Bigram and related non-topic models.