Timbre Difference Capturing in Anomalous Sound Detection

This paper proposes a framework of explaining anomalous machine sounds in the context of anomalous sound detection~(ASD). While ASD has been extensively explored, identifying how anomalous sounds differ from normal sounds is also beneficial for machine condition monitoring. However, existing sound difference captioning methods require anomalous sounds for training, which is impractical in typical machine condition monitoring settings where such sounds are unavailable. To solve this issue, we propose a new strategy for explaining anomalous differences that does not require anomalous sounds for training. Specifically, we introduce a framework that explains differences in predefined timbre attributes instead of using free-form text captions. Objective metrics of timbre attributes can be computed using timbral models developed through psycho-acoustical research, enabling the estimation of how and what timbre attributes have changed from normal sounds without training machine learning models. Additionally, to accurately determine timbre differences regardless of variations in normal training data, we developed a method that jointly conducts anomalous sound detection and timbre difference estimation based on a k-nearest neighbors method in an audio embedding space. Evaluation using the MIMII DG dataset demonstrated the effectiveness of the proposed method.

MIMII-Gen: Generative Modeling Approach for Simulated Evaluation of Anomalous Sound Detection System

Insufficient recordings and the scarcity of anomalies present significant challenges in developing and validating robust anomaly detection systems for machine sounds. To address these limitations, we propose a novel approach for generating diverse anomalies in machine sound using a latent diffusion-based model that integrates an encoder-decoder framework. Our method utilizes the Flan-T5 model to encode captions derived from audio file metadata, enabling conditional generation through a carefully designed U-Net architecture. This approach aids our model in generating audio signals within the EnCodec latent space, ensuring high contextual relevance and quality. We objectively evaluated the quality of our generated sounds using the Fr\'echet Audio Distance (FAD) score and other metrics, demonstrating that our approach surpasses existing models in generating reliable machine audio that closely resembles actual abnormal conditions. The evaluation of the anomaly detection system using our generated data revealed a strong correlation, with the area under the curve (AUC) score differing by 4.8\% from the original, validating the effectiveness of our generated data. These results demonstrate the potential of our approach to enhance the evaluation and robustness of anomaly detection systems across varied and previously unseen conditions. Audio samples can be found at \url{https://hpworkhub.github.io/MIMII-Gen.github.io/}.

Domain-Independent Automatic Generation of Descriptive Texts for Time-Series Data

Due to scarcity of time-series data annotated with descriptive texts, training a model to generate descriptive texts for time-series data is challenging. In this study, we propose a method to systematically generate domain-independent descriptive texts from time-series data. We identify two distinct approaches for creating pairs of time-series data and descriptive texts: the forward approach and the backward approach. By implementing the novel backward approach, we create the Temporal Automated Captions for Observations (TACO) dataset. Experimental results demonstrate that a contrastive learning based model trained using the TACO dataset is capable of generating descriptive texts for time-series data in novel domains.

Description and Discussion on DCASE 2024 Challenge Task 2: First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring

We present the task description of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2024 Challenge Task 2: First-shot unsupervised anomalous sound detection (ASD) for machine condition monitoring. Continuing from last year's DCASE 2023 Challenge Task 2, we organize the task as a first-shot problem under domain generalization required settings. The main goal of the first-shot problem is to enable rapid deployment of ASD systems for new kinds of machines without the need for machine-specific hyperparameter tunings. This problem setting was realized by (1) giving only one section for each machine type and (2) having completely different machine types for the development and evaluation datasets. For the DCASE 2024 Challenge Task 2, data of completely new machine types were newly collected and provided as the evaluation dataset. In addition, attribute information such as the machine operation conditions were concealed for several machine types to mimic situations where such information are unavailable. We will add challenge results and analysis of the submissions after the challenge submission deadline.

Description and Discussion on DCASE 2023 Challenge Task 2: First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring

We present the task description of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2023 Challenge Task 2: "First-shot unsupervised anomalous sound detection (ASD) for machine condition monitoring". The main goal is to enable rapid deployment of ASD systems for new kinds of machines using only a few normal samples, without the need for hyperparameter tuning. In the past ASD tasks, developed methods tuned hyperparameters for each machine type, as the development and evaluation datasets had the same machine types. However, collecting normal and anomalous data as the development dataset can be infeasible in practice. In 2023 Task 2, we focus on solving first-shot problem, which is the challenge of training a model on a few machines of a completely novel machine type. Specifically, (i) each machine type has only one section, and (ii) machine types in the development and evaluation datasets are completely different. We will add challenge results and analysis of the submissions after the challenge submission deadline.

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 of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2022 Challenge Task 2: "Unsupervised anomalous sound detection (ASD) for machine condition monitoring applying domain generalization techniques". Domain shifts are a critical problem for the application of ASD systems. Because domain shifts can change the acoustic characteristics of data, a model trained in a source domain performs poorly for a target domain. In DCASE 2021 Challenge Task 2, we organized an ASD task for handling domain shifts. In this task, it was assumed that the occurrences of domain shifts are known. However, in practice, the domain of each sample may not be given, and the domain shifts can occur implicitly. In 2022 Task 2, we focus on domain generalization techniques that detects anomalies regardless of the domain shifts. Specifically, the domain of each sample is not given in the test data and only one threshold is allowed for all domains. We will add challenge results and analysis of the submissions after the challenge submission deadline.

Hierarchical Conditional Variational Autoencoder Based Acoustic Anomaly Detection

This paper aims to develop an acoustic signal-based unsupervised anomaly detection method for automatic machine monitoring. Existing approaches such as deep autoencoder (DAE), variational autoencoder (VAE), conditional variational autoencoder (CVAE) etc. have limited representation capabilities in the latent space and, hence, poor anomaly detection performance. Different models have to be trained for each different kind of machines to accurately perform the anomaly detection task. To solve this issue, we propose a new method named as hierarchical conditional variational autoencoder (HCVAE). This method utilizes available taxonomic hierarchical knowledge about industrial facility to refine the latent space representation. This knowledge helps model to improve the anomaly detection performance as well. We demonstrated the generalization capability of a single HCVAE model for different types of machines by using appropriate conditions. Additionally, to show the practicability of the proposed approach, (i) we evaluated HCVAE model on different domain and (ii) we checked the effect of partial hierarchical knowledge. Our results show that HCVAE method validates both of these points, and it outperforms the baseline system on anomaly detection task by utmost 15 % on the AUC score metric.

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). To handle performance degradation caused by domain shifts that are difficult to detect or too frequent to adapt, domain generalization techniques are preferred. However, currently available datasets have difficulties in evaluating these techniques, such as limited number of values for parameters that cause domain shifts (domain shift parameters). In this paper, we present the first ASD dataset for the domain generalization techniques, called MIMII DG. The dataset consists of five machine types and three domain shift scenarios for each machine type. We prepared at least two values for the domain shift parameters in the source domain. Also, we introduced domain shifts that can be difficult to notice. Experimental results using two baseline systems indicate that the dataset reproduces the domain shift scenarios and is useful for benchmarking domain generalization techniques.

Description and Discussion on DCASE 2021 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring under Domain Shifted Conditions

We present the task description and discussion on the results of the DCASE 2021 Challenge Task 2. Last year, we organized unsupervised anomalous sound detection (ASD) task; identifying whether the given sound is normal or anomalous without anomalous training data. In this year, we organize an advanced unsupervised ASD task under domain-shift conditions which focuses on the inevitable problem for the practical use of ASD systems. The main challenge of this task is to detect unknown anomalous sounds where the acoustic characteristics of the training and testing samples are different, i.e. domain-shifted. This problem is frequently occurs due to changes in seasons, manufactured products, and/or environmental noise. After the challenge submission deadline, we will add challenge results and analysis of the submissions.

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 a new dataset for malfunctioning industrial machine investigation and inspection with domain shifts due to changes in operational and environmental conditions (MIMII DUE). Conventional methods for anomalous sound detection face challenges in practice because the distribution of features changes between the training and operational phases (called domain shift) due to some real-world factors. To check the robustness against domain shifts, we need a dataset with domain shifts, but such a dataset does not exist so far. The new dataset consists of normal and abnormal operating sounds of industrial machines of five different types under two different operational/environmental conditions (source domain and target domain) independent of normal/abnormal, with domain shifts occurring between the two domains. Experimental results show significant performance differences between the source and target domains, and the dataset contains the domain shifts. These results indicate that the dataset will be helpful to check the robustness against domain shifts. The dataset is a subset of the dataset for DCASE 2021 Challenge Task 2 and freely available for download at https://zenodo.org/record/4740355