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Found 22 papers, 13 papers with code
Masked Modeling Duo: Towards a Universal Audio Pre-training Framework
This study proposes Masked Modeling Duo (M2D), an improved masked prediction SSL, which learns by predicting representations of masked input signals that serve as training signals.
Deep Attentive Time Warping
Unlike other learnable models using DTW for warping, our model predicts all local correspondences between two time series and is trained based on metric learning, which enables it to learn the optimal data-dependent warping for the target task.
Audio Difference Captioning Utilizing Similarity-Discrepancy Disentanglement
We proposed Audio Difference Captioning (ADC) as a new extension task of audio captioning for describing the semantic differences between input pairs of similar but slightly different audio clips.
Masked Modeling Duo for Speech: Specializing General-Purpose Audio Representation to Speech using Denoising Distillation
Self-supervised learning general-purpose audio representations have demonstrated high performance in a variety of tasks.
Masked Modeling Duo: Learning Representations by Encouraging Both Networks to Model the Input
We propose a new method, Masked Modeling Duo (M2D), that learns representations directly while obtaining training signals using only masked patches.
Ranked #1 on
Speaker Identification
on VoxCeleb1
(using extra training data)
Reflectance-Guided, Contrast-Accumulated Histogram Equalization
Existing image enhancement methods fall short of expectations because with them it is difficult to improve global and local image contrast simultaneously.
Reflectance-Oriented Probabilistic Equalization for Image Enhancement
Despite recent advances in image enhancement, it remains difficult for existing approaches to adaptively improve the brightness and contrast for both low-light and normal-light images.
ConceptBeam: Concept Driven Target Speech Extraction
We use it to bridge modality-dependent information, i. e., the speech segments in the mixture, and the specified, modality-independent concept.
Introducing Auxiliary Text Query-modifier to Content-based Audio Retrieval
While the range of conventional content-based audio retrieval is limited to audio that is similar to the query audio, the proposed method can adjust the retrieval range by adding an embedding of the auxiliary text query-modifier to the embedding of the query sample audio in a shared latent space.
Composing General Audio Representation by Fusing Multilayer Features of a Pre-trained Model
This approach improves the utility of frequency and channel information in downstream processes, and combines the effectiveness of middle and late layer features for different tasks.
Masked Spectrogram Modeling using Masked Autoencoders for Learning General-purpose Audio Representation
In this paper, we seek to learn audio representations from the input itself as supervision using a pretext task of auto-encoding of masked spectrogram patches, Masked Spectrogram Modeling (MSM, a variant of Masked Image Modeling applied to audio spectrogram).
BYOL for Audio: Exploring Pre-trained General-purpose Audio Representations
In this study, we hypothesize that representations effective for general audio tasks should provide multiple aspects of robust features of the input sound.
Attention to Warp: Deep Metric Learning for Multivariate Time Series
This approach adapts a parameterized attention model to time warping for greater and more adaptive temporal invariance.
BYOL for Audio: Self-Supervised Learning for General-Purpose Audio Representation
Inspired by the recent progress in self-supervised learning for computer vision that generates supervision using data augmentations, we explore a new general-purpose audio representation learning approach.
Effects of Word-frequency based Pre- and Post- Processings for Audio Captioning
The system we used for Task 6 (Automated Audio Captioning)of the Detection and Classification of Acoustic Scenes and Events(DCASE) 2020 Challenge combines three elements, namely, dataaugmentation, multi-task learning, and post-processing, for audiocaptioning.
The NTT DCASE2020 Challenge Task 6 system: Automated Audio Captioning with Keywords and Sentence Length Estimation
This technical report describes the system participating to the Detection and Classification of Acoustic Scenes and Events (DCASE) 2020 Challenge, Task 6: automated audio captioning.
Ranked #4 on
Audio captioning
on Clotho
Generative Adversarial Image Synthesis with Decision Tree Latent Controller
This paper proposes the decision tree latent controller generative adversarial network (DTLC-GAN), an extension of a GAN that can learn hierarchically interpretable representations without relying on detailed supervision.
Knowledge Discovery from Layered Neural Networks based on Non-negative Task Decomposition
Interpretability has become an important issue in the machine learning field, along with the success of layered neural networks in various practical tasks.
Understanding Community Structure in Layered Neural Networks
We show experimentally that our proposed method can reveal the role of each part of a layered neural network by applying the neural networks to three types of data sets, extracting communities from the trained network, and applying the proposed method to the community structure.
Generative Attribute Controller With Conditional Filtered Generative Adversarial Networks
This controller is based on a novel generative model called the conditional filtered generative adversarial network (CFGAN), which is an extension of the conventional conditional GAN (CGAN) that incorporates a filtering architecture into the generator input.
Modular Representation of Layered Neural Networks
And (3) data analysis: in practical data it reveals the community structure in the input, hidden, and output layers, which serves as a clue for discovering knowledge from a trained neural network.
Adaptive Dither Voting for Robust Spatial Verification
Hough voting in a geometric transformation space allows us to realize spatial verification, but remains sensitive to feature detection errors because of the inflexible quantization of single feature correspondences.
