10 accepted papers
This paper describes a music arrangement method of popular music that can convert a band score into a piano score with a steplessly-specified level of performance difficulty. The basic strategy of band-to-piano score arrangement is to select notes from an augmented band score obtained by up- and dow…
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 presents an improvement on an existing grammar-based method for metrical structure detection and alignment, a task which involves aligning a repeated tree structure with an input stream of musical notes. The previous method achieves state-of-the-art results, but performs poorly when it la…
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…
Most work on automatic transcription produces “piano roll” data with no musical interpretation of the rhythm or pitches. We present a polyphonic transcription method that converts a music audio signal into a human-readable musical score, by integrating multi-pitch detection and rhythm quantization m…
This paper presents a Bayesian extension of multichannel nonnegative matrix factorization (MNMF) that decomposes the complex spectrograms of mixture signals recorded by a microphone array into basis spectra, their temporal activations, and the spatial correlation matrices of sources (directions) in…
This paper presents a probabilistic formulation of music language modelling based on the generative theory of tonal music (GTTM) named probabilistic GTTM (PGTTM). GTTM is a well-known music theory that describes the tree structure of written music in analogy with the phrase structure grammar of natu…