137 citations · 311 across the 28 of their papers we have counts for
31 papers
SIRUP: A diffusion-based virtual upmixer of steering vectors for highly-directive spatialization with first-order ambisonics
Emilio Picard, Diego Di Carlo, Aditya Arie Nugraha +2
This paper presents virtual upmixing of steering vectors captured by a fewer-channel spherical microphone array. This challenge has conventionally been addressed by recovering the…
Gaussian Process Regression of Steering Vectors With Physics-Aware Deep Composite Kernels for Augmented Listening
Diego Di Carlo, Shoichi Koyama, Nugraha Aditya Arie +3
This paper investigates continuous representations of steering vectors over frequency and microphone/source positions for augmented listening (e.g., spatial filtering and binaural…
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…
Neural Steerer: Novel Steering Vector Synthesis with a Causal Neural Field over Frequency and Source Positions
Diego Di Carlo, Aditya Arie Nugraha, Mathieu Fontaine +1
We address the problem of accurately interpolating measured anechoic steering vectors with a deep learning framework called the neural field. This task plays a pivotal role in redu…
DNN-Free Low-Latency Adaptive Speech Enhancement Based on Frame-Online Beamforming Powered by Block-Online FastMNMF
Aditya Arie Nugraha, Kouhei Sekiguchi, Mathieu Fontaine +2
This paper describes a practical dual-process speech enhancement system that adapts environment-sensitive frame-online beamforming (front-end) with help from environment-free block…
Direction-Aware Adaptive Online Neural Speech Enhancement with an Augmented Reality Headset in Real Noisy Conversational Environments
Kouhei Sekiguchi, Aditya Arie Nugraha, Yicheng Du +3
This paper describes the practical response- and performance-aware development of online speech enhancement for an augmented reality (AR) headset that helps a user understand conve…
Direction-Aware Joint Adaptation of Neural Speech Enhancement and Recognition in Real Multiparty Conversational Environments
Yicheng Du, Aditya Arie Nugraha, Kouhei Sekiguchi +3
This paper describes noisy speech recognition for an augmented reality headset that helps verbal communication within real multiparty conversational environments. A major approach…
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…
End-to-end Music-mixed Speech Recognition
Jeongwoo Woo, Masato Mimura, Kazuyoshi Yoshii +1
Automatic speech recognition (ASR) in multimedia content is one of the promising applications, but speech data in this kind of content are frequently mixed with background music, w…
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…
MirrorNet: A Deep Bayesian Approach to Reflective 2D Pose Estimation from Human Images
Takayuki Nakatsuka, Kazuyoshi Yoshii, Yuki Koyama +3
This paper proposes a statistical approach to 2D pose estimation from human images. The main problems with the standard supervised approach, which is based on a deep recognition (i…
Multi-Step Chord Sequence Prediction Based on Aggregated Multi-Scale Encoder-Decoder Network
Tristan Carsault, Andrew McLeod, Philippe Esling +3
This paper studies the prediction of chord progressions for jazz music by relying on machine learning models. The motivation of our study comes from the recent success of neural ne…
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…
Statistical Learning and Estimation of Piano Fingering
Eita Nakamura, Yasuyuki Saito, Kazuyoshi Yoshii
Automatic estimation of piano fingering is important for understanding the computational process of music performance and applicable to performance assistance and education systems…
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…