9 accepted papers
This paper proposes a method for embedding diffusion potentials into a hyperbolic space in order to visualize the differentiation structure consisting of diffusion and branching inherent in high-dimensional data. In recent years, the rapid development of single-cell sequencing in the field of biolog…
This paper proposes a Bayesian nonparametric diffusion model with a black-box warping function represented by a Gaussian process to infer potential diffusion structures latent in observed data, such as differentiation mechanisms of living cells and phylogenetic evolution processes of media informati…
This paper addresses the question, ”What is the smallest object that contains all rectangular partitions with n or fewer blocks?” and shows its application to relational data analysis using a new strategy we call super Bayes as an alternative to Bayesian nonparametric (BNP) methods. Conventionally,…
This paper describes a representation learning method for disentangling an arbitrary musical instrument sound into latent pitch and timbre representations. Although such pitch-timbre disentanglement has been achieved with a variational autoencoder (VAE), especially for a predefined set of musical in…
This paper describes a statistical post-processing method for automatic singing transcription that corrects pitch and rhythm errors included in a transcribed note sequence. Although the performance of frame-level pitch estimation has been improved drastically by deep learning techniques, note-level…
This paper describes neural singing transcription that estimates a sequence of musical notes directly from the audio signal of singing voice in an end-to-end manner without time-aligned training data. A conventional approach to singing transcription is to perform vocal F0 estimation followed by musi…
This paper describes a statistical method of automatic drum transcription that estimates a musical score of bass and snare drums and hi-hats from a drum signal separated from a popular music signal. One of the most effective approaches for this problem is to apply nonnegative matrix factor deconvolu…
This paper describes a statistical method for estimating musical scores for lead, bass, and rhythm guitars from polyphonic audio signals of typical band-style music. To perform multi-instrument transcription involving multi-pitch detection and part assignment, it is crucial to formulate a musical la…
We study a method for converting the music style of a given melody to a target style (e.g. from classical music style to pop music style) based on unsupervised statistical learning. Following the analogy with machine translation, we propose a statistical formulation of style conversion based on inte…