10 accepted papers
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…
One of the challenges in deploying a machine learning model is that the model’s performance degrades as the operating environment changes. To maintain the performance, streaming active learning is used, in which the model is retrained by adding a newly annotated sample to the training dataset if the…
Semi-supervised anomaly detection (SSAD) is a task where normal data and a limited number of anomalous data are available for training. In practical situations, SSAD methods suffer adapting to domain shifts, since anomalous data are unlikely to be available for the target domain in the training phas…
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. We propose an environmental-sound-extraction method using onomatopoeic words to specify the target sound to be extracted. B…
Recent progress on end-to-end neural diarization (EEND) has en-abled overlap-aware speaker diarization with a single neural net-work. This paper proposes to enhance EEND by using multi-channel signals from distributed microphones. We replace Transformer en-coders in EEND with two types of encoders t…
To develop a machine sound monitoring system, a method for detecting anomalous sound is proposed. Exact likelihood estimation using Normalizing Flows is a promising technique for unsupervised anomaly detection, but it can fail at out-of-distribution detection since the likelihood is affected by the…
As the labor force decreases, the demand for labor-saving automatic anomalous sound detection technology that conducts maintenance of industrial equipment has grown. Conventional approaches detect anomalies based on the reconstruction errors of an autoencoder. However, when the target machine sound…
To develop a sound-monitoring system for checking machine health, a method for detecting anomalous sounds is proposed. In real environments such as factories, reverberation and background noise are mixed in an observed signal, so detection performance is degraded. It can be expected that detection p…
Independent low-rank matrix analysis (ILRMA), a unified method of independent vector analysis (IVA) and nonnegative matrix factorization (NMF), is a state-of-the-art blind source separation method for convolutive mixtures. Although ILRMA provides high separation performance for music signals whose s…
A new method for solving adaptive Boolean compressive sensing is proposed. By greedy maximization of an expected information gain, a conventional method controls the pool-size for adaptive Boolean compressive sensing. However, the conventional greedy method has the drawback that it has no guarantee…