Search Results for author:
Found 26 papers, 14 papers with code
Multi-scale Multi-band DenseNets for Audio Source Separation
This paper deals with the problem of audio source separation.
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
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).
AENet: Learning Deep Audio Features for Video Analysis
Instead, combining visual features with our AENet features, which can be computed efficiently on a GPU, leads to significant performance improvements on action recognition and video highlight detection.
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.
End-to-end lyrics Recognition with Voice to Singing Style Transfer
This approach, called voice to singing (V2S), performs the voice style conversion by modulating the F0 contour of the natural speech with that of a singing voice.
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.
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
Automatic Pronunciation Generation by Utilizing a Semi-supervised Deep Neural Networks
Phonemic or phonetic sub-word units are the most commonly used atomic elements to represent speech signals in modern ASRs.
Deep Convolutional Neural Networks and Data Augmentation for Acoustic Event Detection
We propose a novel method for Acoustic Event Detection (AED).
Sound Multimedia
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.
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.
Leveraging Symmetrical Convolutional Transformer Networks for Speech to Singing Voice Style Transfer
In this paper, we propose a model to perform style transfer of speech to singing voice.
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.
Nonparallel Emotional Voice Conversion For Unseen Speaker-Emotion Pairs Using Dual Domain Adversarial Network & Virtual Domain Pairing
Primary goal of an emotional voice conversion (EVC) system is to convert the emotion of a given speech signal from one style to another style without modifying the linguistic content of the signal.
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.
Iteratively Improving Speech Recognition and Voice Conversion
Many existing works on voice conversion (VC) tasks use automatic speech recognition (ASR) models for ensuring linguistic consistency between source and converted samples.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
