Search Results for author:
Found 57 papers, 31 papers with code
Multi-scale Multi-band DenseNets for Audio Source Separation
This paper deals with the problem of audio source separation.
All for One and One for All: Improving Music Separation by Bridging Networks
This paper proposes several improvements for music separation with deep neural networks (DNNs), namely a multi-domain loss (MDL) and two combination schemes.
Ranked #21 on
Music Source Separation
on MUSDB18
The Whole Is Greater than the Sum of Its Parts: Improving DNN-based Music Source Separation
We modify the target network, i. e., the network architecture of the original DNN-based MSS, by adding bridging paths for each output instrument to share their information.
MMDenseLSTM: An efficient combination of convolutional and recurrent neural networks for audio source separation
Deep neural networks have become an indispensable technique for audio source separation (ASS).
Ranked #17 on
Music Source Separation
on MUSDB18
(using extra training data)
Music Source Separation
Sound
Audio and Speech Processing
The Sound Demixing Challenge 2023 $\unicode{x2013}$ Music Demixing Track
We propose a formalization of the errors that can occur in the design of a training dataset for MSS systems and introduce two new datasets that simulate such errors: SDXDB23_LabelNoise and SDXDB23_Bleeding1.
D3Net: Densely connected multidilated DenseNet for music source separation
In this paper, we claim the importance of a rapid growth of a receptive field and a simultaneous modeling of multi-resolution data in a single convolution layer, and propose a novel CNN architecture called densely connected dilated DenseNet (D3Net).
Ranked #12 on
Music Source Separation
on MUSDB18
(using extra training data)
Densely connected multidilated convolutional networks for dense prediction tasks
In this paper, we claim the importance of a dense simultaneous modeling of multiresolution representation and propose a novel CNN architecture called densely connected multidilated DenseNet (D3Net).
Ranked #47 on
Semantic Segmentation
on Cityscapes test
Densely Connected Multi-Dilated Convolutional Networks for Dense Prediction Tasks
In this paper, we claim the importance of a dense simultaneous modeling of multiresolution representation and propose a novel CNN architecture called densely connected multidilated DenseNet (D3Net).
BigVSAN: Enhancing GAN-based Neural Vocoders with Slicing Adversarial Network
In the literature, it has been demonstrated that slicing adversarial network (SAN), an improved GAN training framework that can find the optimal projection, is effective in the image generation task.
Ranked #2 on
Speech Synthesis
on LibriTTS
SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization
In this paper, we propose a new training scheme that extends the standard VAE via novel stochastic dequantization and quantization, called stochastically quantized variational autoencoder (SQ-VAE).
Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion
Consistency Models (CM) (Song et al., 2023) accelerate score-based diffusion model sampling at the cost of sample quality but lack a natural way to trade-off quality for speed.
Ranked #2 on
Image Generation
on CIFAR-10
Music Mixing Style Transfer: A Contrastive Learning Approach to Disentangle Audio Effects
We propose an end-to-end music mixing style transfer system that converts the mixing style of an input multitrack to that of a reference song.
Automatic music mixing with deep learning and out-of-domain data
Music mixing traditionally involves recording instruments in the form of clean, individual tracks and blending them into a final mixture using audio effects and expert knowledge (e. g., a mixing engineer).
The Sound Demixing Challenge 2023 $\unicode{x2013}$ Cinematic Demixing Track
A significant source of this improvement was making the simulated data better match real cinematic audio, which we further investigate in detail.
Automatic Piano Transcription with Hierarchical Frequency-Time Transformer
This is especially helpful when determining the precise onset and offset for each note in the polyphonic piano content.
Diffiner: A Versatile Diffusion-based Generative Refiner for Speech Enhancement
Although deep neural network (DNN)-based speech enhancement (SE) methods outperform the previous non-DNN-based ones, they often degrade the perceptual quality of generated outputs.
Multi-ACCDOA: Localizing and Detecting Overlapping Sounds from the Same Class with Auxiliary Duplicating Permutation Invariant Training
The multi- ACCDOA format (a class- and track-wise output format) enables the model to solve the cases with overlaps from the same class.
STARSS22: A dataset of spatial recordings of real scenes with spatiotemporal annotations of sound events
Additionally, the report presents the baseline system that accompanies the dataset in the challenge with emphasis on the differences with the baseline of the previous iterations; namely, introduction of the multi-ACCDOA representation to handle multiple simultaneous occurences of events of the same class, and support for additional improved input features for the microphone array format.
Ranked #1 on
Sound Event Localization and Detection
on STARSS22
ACCDOA: Activity-Coupled Cartesian Direction of Arrival Representation for Sound Event Localization and Detection
Conventional NN-based methods use two branches for a sound event detection (SED) target and a direction-of-arrival (DOA) target.
SAN: Inducing Metrizability of GAN with Discriminative Normalized Linear Layer
Generative adversarial networks (GANs) learn a target probability distribution by optimizing a generator and a discriminator with minimax objectives.
Ranked #1 on
Image Generation
on FFHQ 1024 x 1024
Music Demixing Challenge 2021
The main differences compared with the past challenges are 1) the competition is designed to more easily allow machine learning practitioners from other disciplines to participate, 2) evaluation is done on a hidden test set created by music professionals dedicated exclusively to the challenge to assure the transparency of the challenge, i. e., the test set is not accessible from anyone except the challenge organizers, and 3) the dataset provides a wider range of music genres and involved a greater number of mixing engineers.
CLIPSep: Learning Text-queried Sound Separation with Noisy Unlabeled Videos
Further, videos in the wild often contain off-screen sounds and background noise that may hinder the model from learning the desired audio-textual correspondence.
STARSS23: An Audio-Visual Dataset of Spatial Recordings of Real Scenes with Spatiotemporal Annotations of Sound Events
While direction of arrival (DOA) of sound events is generally estimated from multichannel audio data recorded in a microphone array, sound events usually derive from visually perceptible source objects, e. g., sounds of footsteps come from the feet of a walker.
PeaCoK: Persona Commonsense Knowledge for Consistent and Engaging Narratives
They must also learn to maintain consistent speaker personas for themselves throughout the narrative, so that their counterparts feel involved in a realistic conversation or story.
GibbsDDRM: A Partially Collapsed Gibbs Sampler for Solving Blind Inverse Problems with Denoising Diffusion Restoration
Pre-trained diffusion models have been successfully used as priors in a variety of linear inverse problems, where the goal is to reconstruct a signal from noisy linear measurements.
Improving Voice Separation by Incorporating End-to-end Speech Recognition
Despite recent advances in voice separation methods, many challenges remain in realistic scenarios such as noisy recording and the limits of available data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
FP-Diffusion: Improving Score-based Diffusion Models by Enforcing the Underlying Score Fokker-Planck Equation
Score-based generative models (SGMs) learn a family of noise-conditional score functions corresponding to the data density perturbed with increasingly large amounts of noise.
ComFact: A Benchmark for Linking Contextual Commonsense Knowledge
Understanding rich narratives, such as dialogues and stories, often requires natural language processing systems to access relevant knowledge from commonsense knowledge graphs.
Improving DNN-based Music Source Separation using Phase Features
Music source separation with deep neural networks typically relies only on amplitude features.
Spatial mixup: Directional loudness modification as data augmentation for sound event localization and detection
Data augmentation methods have shown great importance in diverse supervised learning problems where labeled data is scarce or costly to obtain.
DiffuCOMET: Contextual Commonsense Knowledge Diffusion
Inferring contextually-relevant and diverse commonsense to understand narratives remains challenging for knowledge models.
AR-ELBO: Preventing Posterior Collapse Induced by Oversmoothing in Gaussian VAE
Variational autoencoders (VAEs) often suffer from posterior collapse, which is a phenomenon that the learned latent space becomes uninformative.
Adversarial attacks on audio source separation
Despite the excellent performance of neural-network-based audio source separation methods and their wide range of applications, their robustness against intentional attacks has been largely neglected.
Hierarchical disentangled representation learning for singing voice conversion
Conventional singing voice conversion (SVC) methods often suffer from operating in high-resolution audio owing to a high dimensionality of data.
Preventing Oversmoothing in VAE via Generalized Variance Parameterization
Variational autoencoders (VAEs) often suffer from posterior collapse, which is a phenomenon in which the learned latent space becomes uninformative.
Training Speech Enhancement Systems with Noisy Speech Datasets
Furthermore, we propose a noise augmentation scheme for mixture-invariant training (MixIT), which allows using it also in such scenarios.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Ensemble of ACCDOA- and EINV2-based Systems with D3Nets and Impulse Response Simulation for Sound Event Localization and Detection
This report describes our systems submitted to the DCASE2021 challenge task 3: sound event localization and detection (SELD) with directional interference.
Automatic DJ Transitions with Differentiable Audio Effects and Generative Adversarial Networks
A central task of a Disc Jockey (DJ) is to create a mixset of mu-sic with seamless transitions between adjacent tracks.
Distortion Audio Effects: Learning How to Recover the Clean Signal
Given the recent advances in music source separation and automatic mixing, removing audio effects in music tracks is a meaningful step toward developing an automated remixing system.
DiffRoll: Diffusion-based Generative Music Transcription with Unsupervised Pretraining Capability
In this paper we propose a novel generative approach, DiffRoll, to tackle automatic music transcription (AMT).
Hierarchical Diffusion Models for Singing Voice Neural Vocoder
Recent progress in deep generative models has improved the quality of neural vocoders in speech domain.
Robust One-Shot Singing Voice Conversion
We then propose a two-stage training method called Robustify that train the one-shot SVC model in the first stage on clean data to ensure high-quality conversion, and introduces enhancement modules to the encoders of the model in the second stage to enhance the feature extraction from distorted singing voices.
Unsupervised vocal dereverberation with diffusion-based generative models
Removing reverb from reverberant music is a necessary technique to clean up audio for downstream music manipulations.
Cross-modal Face- and Voice-style Transfer
Image-to-image translation and voice conversion enable the generation of a new facial image and voice while maintaining some of the semantics such as a pose in an image and linguistic content in audio, respectively.
Diffusion-based Signal Refiner for Speech Separation
We experimentally show that our refiner can provide a clearer harmonic structure of speech and improves the reference-free metric of perceptual quality for arbitrary preceding model architectures.
Diffusion-Based Speech Enhancement with Joint Generative and Predictive Decoders
At the decoded feature level, we fuse the two decoded features by generative and predictive decoders.
On the Equivalence of Consistency-Type Models: Consistency Models, Consistent Diffusion Models, and Fokker-Planck Regularization
The emergence of various notions of ``consistency'' in diffusion models has garnered considerable attention and helped achieve improved sample quality, likelihood estimation, and accelerated sampling.
Enhancing Semantic Communication with Deep Generative Models -- An ICASSP Special Session Overview
Semantic communication is poised to play a pivotal role in shaping the landscape of future AI-driven communication systems.
VRDMG: Vocal Restoration via Diffusion Posterior Sampling with Multiple Guidance
Restoring degraded music signals is essential to enhance audio quality for downstream music manipulation.
Timbre-Trap: A Low-Resource Framework for Instrument-Agnostic Music Transcription
Several works have explored multi-instrument transcription as a means to bolster the performance of models on low-resource tasks, but these methods face the same data availability issues.
Towards reporting bias in visual-language datasets: bimodal augmentation by decoupling object-attribute association
In this paper, we focus on the wide existence of reporting bias in visual-language datasets, embodied as the object-attribute association, which can subsequentially degrade models trained on them.
On the Language Encoder of Contrastive Cross-modal Models
Contrastive cross-modal models such as CLIP and CLAP aid various vision-language (VL) and audio-language (AL) tasks.
Manifold Preserving Guided Diffusion
Despite the recent advancements, conditional image generation still faces challenges of cost, generalizability, and the need for task-specific training.
HQ-VAE: Hierarchical Discrete Representation Learning with Variational Bayes
Vector quantization (VQ) is a technique to deterministically learn features with discrete codebook representations.
MusicMagus: Zero-Shot Text-to-Music Editing via Diffusion Models
This paper introduces a novel approach to the editing of music generated by such models, enabling the modification of specific attributes, such as genre, mood and instrument, while maintaining other aspects unchanged.
MR-MT3: Memory Retaining Multi-Track Music Transcription to Mitigate Instrument Leakage
This paper presents enhancements to the MT3 model, a state-of-the-art (SOTA) token-based multi-instrument automatic music transcription (AMT) model.
Automated Black-box Prompt Engineering for Personalized Text-to-Image Generation
Prompt engineering is effective for controlling the output of text-to-image (T2I) generative models, but it is also laborious due to the need for manually crafted prompts.
