137 citations · 311 across the 28 of their papers we have counts for
17 papers · 1 filter
SHAMaNS: Sound Localization with Hybrid Alpha-Stable Spatial Measure and Neural Steerer
Diego Di Carlo, Mathieu Fontaine, Aditya Arie Nugraha +2
This paper describes a sound source localization (SSL) technique that combines an -stable model for the observed signal with a neural network-based approach for modeling steerin…
Run-Time Adaptation of Neural Beamforming for Robust Speech Dereverberation and Denoising
Yoto Fujita, Aditya Arie Nugraha, Diego Di Carlo +3
This paper describes speech enhancement for realtime automatic speech recognition (ASR) in real environments. A standard approach to this task is to use neural beamforming that can…
DOA-Aware Audio-Visual Self-Supervised Learning for Sound Event Localization and Detection
Yoto Fujita, Yoshiaki Bando, Keisuke Imoto +2
This paper describes sound event localization and detection (SELD) for spatial audio recordings captured by firstorder ambisonics (FOA) microphones. In this task, one may train a d…
Neural Fast Full-Rank Spatial Covariance Analysis for Blind Source Separation
Yoshiaki Bando, Yoshiki Masuyama, Aditya Arie Nugraha +1
This paper describes an efficient unsupervised learning method for a neural source separation model that utilizes a probabilistic generative model of observed multichannel mixtures…
Generalized Fast Multichannel Nonnegative Matrix Factorization Based on Gaussian Scale Mixtures for Blind Source Separation
Mathieu Fontaine, Kouhei Sekiguchi, Aditya Nugraha +2
This paper describes heavy-tailed extensions of a state-of-the-art versatile blind source separation method called fast multichannel nonnegative matrix factorization (FastMNMF) fro…
Global Structure-Aware Drum Transcription Based on Self-Attention Mechanisms
Ryoto Ishizuka, Ryo Nishikimi, Kazuyoshi Yoshii
This paper describes an automatic drum transcription (ADT) method that directly estimates a tatum-level drum score from a music signal, in contrast to most conventional ADT methods…
Tatum-Level Drum Transcription Based on a Convolutional Recurrent Neural Network with Language Model-Based Regularized Training
Ryoto Ishizuka, Ryo Nishikimi, Eita Nakamura +1
This paper describes a neural drum transcription method that detects from music signals the onset times of drums at the level, where tatum times are assumed to be…
The MIDI Degradation Toolkit: Symbolic Music Augmentation and Correction
Andrew McLeod, James Owers, Kazuyoshi Yoshii
In this paper, we introduce the MIDI Degradation Toolkit (MDTK), containing functions which take as input a musical excerpt (a set of notes with pitch, onset time, and duration), a…
Non-Local Musical Statistics as Guides for Audio-to-Score Piano Transcription
Kentaro Shibata, Eita Nakamura, Kazuyoshi Yoshii
We present an automatic piano transcription system that converts polyphonic audio recordings into musical scores. This has been a long-standing problem of music information process…
Semi-supervised Neural Chord Estimation Based on a Variational Autoencoder with Latent Chord Labels and Features
Yiming Wu, Tristan Carsault, Eita Nakamura +1
This paper describes a statistically-principled semi-supervised method of automatic chord estimation (ACE) that can make effective use of music signals regardless of the availabili…
Deep Bayesian Unsupervised Source Separation Based on a Complex Gaussian Mixture Model
Yoshiaki Bando, Yoko Sasaki, Kazuyoshi Yoshii
This paper presents an unsupervised method that trains neural source separation by using only multichannel mixture signals. Conventional neural separation methods require a lot of…
Musical Rhythm Transcription Based on Bayesian Piece-Specific Score Models Capturing Repetitions
Eita Nakamura, Kazuyoshi Yoshii
Most work on musical score models (a.k.a. musical language models) for music transcription has focused on describing the local sequential dependence of notes in musical scores and…
Unsupervised Speech Enhancement Based on Multichannel NMF-Informed Beamforming for Noise-Robust Automatic Speech Recognition
Kazuki Shimada, Yoshiaki Bando, Masato Mimura +3
This paper describes multichannel speech enhancement for improving automatic speech recognition (ASR) in noisy environments. Recently, the minimum variance distortionless response…
A Deep Generative Model of Speech Complex Spectrograms
Aditya Arie Nugraha, Kouhei Sekiguchi, Kazuyoshi Yoshii
This paper proposes an approach to the joint modeling of the short-time Fourier transform magnitude and phase spectrograms with a deep generative model. We assume that the magnitud…
Fast Multichannel Source Separation Based on Jointly Diagonalizable Spatial Covariance Matrices
Kouhei Sekiguchi, Aditya Arie Nugraha, Yoshiaki Bando +1
This paper describes a versatile method that accelerates multichannel source separation methods based on full-rank spatial modeling. A popular approach to multichannel source separ…
Statistical Speech Enhancement Based on Probabilistic Integration of Variational Autoencoder and Non-Negative Matrix Factorization
Yoshiaki Bando, Masato Mimura, Katsutoshi Itoyama +2
This paper presents a statistical method of single-channel speech enhancement that uses a variational autoencoder (VAE) as a prior distribution on clean speech. A standard approach…
Singing Voice Separation and Vocal F0 Estimation based on Mutual Combination of Robust Principal Component Analysis and Subharmonic Summation
Yukara Ikemiya, Katsutoshi Itoyama, Kazuyoshi Yoshii
This paper presents a new method of singing voice analysis that performs mutually-dependent singing voice separation and vocal fundamental frequency (F0) estimation. Vocal F0 estim…