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Found 33 papers, 20 papers with code
Guided Masked Self-Distillation Modeling for Distributed Multimedia Sensor Event Analysis
This is because the information obtained from a single sensor is often missing or fragmented in such an environment; observations from multiple locations and modalities should be integrated to analyze events comprehensively.
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.
Refining Knowledge Transfer on Audio-Image Temporal Agreement for Audio-Text Cross Retrieval
Experimental results show that method (i) improves the audio-text retrieval performance by selecting the nearest image that aligns with the audio information and transferring the learned knowledge.
6DoF SELD: Sound Event Localization and Detection Using Microphones and Motion Tracking Sensors on self-motioning human
A system trained only with a dataset using microphone arrays in a fixed position would be unable to adapt to the fast relative motion of sound events associated with self-motion, resulting in the degradation of SELD performance.
Unrestricted Global Phase Bias-Aware Single-channel Speech Enhancement with Conformer-based Metric GAN
With the rapid development of neural networks in recent years, the ability of various networks to enhance the magnitude spectrum of noisy speech in the single-channel speech enhancement domain has become exceptionally outstanding.
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.
Description and Discussion on DCASE 2023 Challenge Task 2: First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring
In 2023 Task 2, we focus on solving the first-shot problem, which is the challenge of training a model on a completely novel machine type.
Deep sound-field denoiser: optically-measured sound-field denoising using deep neural network
We compared the method with conventional ones, such as image filters, a spatiotemporal filter, and other DNN architectures, on numerical and experimental data.
First-shot anomaly sound detection for machine condition monitoring: A domain generalization baseline
This paper provides a baseline system for First-shot-compliant unsupervised anomaly detection (ASD) for machine condition monitoring.
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)
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.
Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques
We present the task description and discussion on the results of the DCASE 2022 Challenge Task 2: ``Unsupervised anomalous sound detection (ASD) for machine condition monitoring applying domain generalization techniques''.
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.
Multi-view and Multi-modal Event Detection Utilizing Transformer-based Multi-sensor fusion
We tackle a challenging task: multi-view and multi-modal event detection that detects events in a wide-range real environment by utilizing data from distributed cameras and microphones and their weak labels.
Description and Discussion on DCASE 2021 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring under Domain Shifted Conditions
In 2020, we organized an unsupervised anomalous sound detection (ASD) task, identifying whether a given sound was normal or anomalous without anomalous training data.
ToyADMOS2: Another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions
This paper proposes a new large-scale dataset called "ToyADMOS2" for anomaly detection in machine operating sounds (ADMOS).
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
Description and Discussion on DCASE2020 Challenge Task2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring
The main challenge of this task is to detect unknown anomalous sounds under the condition that only normal sound samples have been provided as training data.
Phase reconstruction based on recurrent phase unwrapping with deep neural networks
In the proposed method, DNNs estimate phase derivatives instead of phase itself, which allows us to avoid the sensitivity problem.
Invertible DNN-based nonlinear time-frequency transform for speech enhancement
Therefore, some end-to-end methods used a DNN to learn the linear T-F transform which is much easier to understand.
Audio and Speech Processing Sound
First Order Ambisonics Domain Spatial Augmentation for DNN-based Direction of Arrival Estimation
The same transformation is applied also to the labels, in order to maintain consistency between input data and target labels.
ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection
To build a large-scale dataset for ADMOS, we collected anomalous operating sounds of miniature machines (toys) by deliberately damaging them.
Batch Uniformization for Minimizing Maximum Anomaly Score of DNN-based Anomaly Detection in Sounds
The AE is trained to minimize the sample mean of the anomaly score of normal sounds in a mini-batch.
Deep Griffin-Lim Iteration
This paper presents a novel phase reconstruction method (only from a given amplitude spectrogram) by combining a signal-processing-based approach and a deep neural network (DNN).
AdaFlow: Domain-Adaptive Density Estimator with Application to Anomaly Detection and Unpaired Cross-Domain Translation
To address this difficulty, we propose AdaFlow, a new DNN-based density estimator that can be easily adapted to the change of the distribution.
Trainable Adaptive Window Switching for Speech Enhancement
To overcome this problem, we incorporate AWS into the speech enhancement procedure, and the windowing function of each time-frame is manipulated using a DNN depending on the input signal.
Unsupervised Detection of Anomalous Sound based on Deep Learning and the Neyman-Pearson Lemma
To calculate the TPR in the objective function, we consider that the set of anomalous sounds is the complementary set of normal sounds and simulate anomalous sounds by using a rejection sampling algorithm.
