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Found 21 papers, 8 papers with code
Distributed collaborative anomalous sound detection by embedding sharing
To develop a machine sound monitoring system, a method for detecting anomalous sound is proposed.
Streaming Active Learning for Regression Problems Using Regression via Classification
One of the challenges in deploying a machine learning model is that the model's performance degrades as the operating environment changes.
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.
Zero-shot domain adaptation of anomalous samples for semi-supervised anomaly detection
To solve this problem, we propose a domain adaptation method for SSAD where no anomalous data are available for the target domain.
Updating Only Encoders Prevents Catastrophic Forgetting of End-to-End ASR Models
In this paper, we present an incremental domain adaptation technique to prevent catastrophic forgetting for an end-to-end automatic speech recognition (ASR) model.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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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''.
Hierarchical Conditional Variational Autoencoder Based Acoustic Anomaly Detection
have limited representation capabilities in the latent space and, hence, poor anomaly detection performance.
Online Neural Diarization of Unlimited Numbers of Speakers Using Global and Local Attractors
Finally, to improve online diarization, our method improves the buffer update method and revisits the variable chunk-size training of EEND.
MIMII DG: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection for Domain Generalization Task
We present a machine sound dataset to benchmark domain generalization techniques for anomalous sound detection (ASD).
Anomalous Sound Detection Based on Machine Activity Detection
We have developed an unsupervised anomalous sound detection method for machine condition monitoring that utilizes an auxiliary task -- detecting when the target machine is active.
Environmental Sound Extraction Using Onomatopoeic Words
An onomatopoeic word, which is a character sequence that phonetically imitates a sound, is effective in expressing characteristics of sound such as duration, pitch, and timbre.
Disentangling Physical Parameters for Anomalous Sound Detection Under Domain Shifts
To solve this problem, the proposed method constrains some latent variables of a normalizing flows (NF) model to represent physical parameters, which enables disentanglement of the factors of domain shifts and learning of a latent space that is invariant with respect to these domain shifts.
Multi-Channel End-to-End Neural Diarization with Distributed Microphones
With simulated and real-recorded datasets, we demonstrated that the proposed method outperformed conventional EEND when a multi-channel input was given while maintaining comparable performance with a single-channel input.
Towards Neural Diarization for Unlimited Numbers of Speakers Using Global and Local Attractors
This makes it possible to produce diarization results of a large number of speakers for the whole recording even if the number of output speakers for each subsequence is limited.
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.
MIMII DUE: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection with Domain Shifts due to Changes in Operational and Environmental Conditions
In this paper, we introduce MIMII DUE, a new dataset for malfunctioning industrial machine investigation and inspection with domain shifts due to changes in operational and environmental conditions.
Flow-based Self-supervised Density Estimation for Anomalous Sound Detection
To develop a machine sound monitoring system, a method for detecting anomalous sound is proposed.
Deep Autoencoding GMM-based Unsupervised Anomaly Detection in Acoustic Signals and its Hyper-parameter Optimization
Failures or breakdowns in factory machinery can be costly to companies, so there is an increasing demand for automatic machine inspection.
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.
Anomalous sound detection based on interpolation deep neural network
However, when the target machine sound is non-stationary, a reconstruction error tends to be large independent of an anomaly, and its variations increased because of the difficulty of predicting the edge frames.
MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection
The purpose of releasing the MIMII dataset is to assist the machine-learning and signal-processing community with their development of automated facility maintenance.
