8 accepted papers
Deep neural network (DNN)-based models for environmental sound classification are not robust against a domain to which training data do not belong, that is, out-of-distribution or unseen data. To utilize pretrained models for the unseen domain, adaptation methods, such as finetuning and transfer lea…
Optical fiber sensing is a technology wherein audio, vibrations, and temperature are detected using an optical fiber; especially the audio/vibrations-aware sensing is called distributed acoustic sensing (DAS). In DAS, observed data, which is comprised of multichannel data, has suffered from severe n…
In many methods of sound event detection (SED), a segmented time frame is regarded as one data sample to model training. The durations of sound events greatly depend on the sound event class, e.g., the sound event "fan" has a long duration, whereas the sound event "mouse clicking" is instantaneous.…
This paper proposes a determined blind source separation method using Bayesian non-parametric modelling of sources. Conventionally source signals are separated from a given set of mixture signals by modelling them using non-negative matrix factorization (NMF). However in NMF, a latent variable signi…
This paper proposes a canonical correlation based feature extraction method with application to anomaly detection in electric appliances. Electric appliances in homes, offices or manufacturing factories are nowadays monitored by Internet of Things (IoT) platforms and systems. For unsupervised anomal…
This paper proposes a scene-dependent anomalous acoustic-event detection based on conditional WaveNet and i-vector. The WaveNet builds normal acoustic event models by exhaustive learning of time-domain signals in the public space to provide scene-independent anomaly detection. I-vectors are used as…
This paper proposes detection of anomaly acoustic scenes based on a temporal dissimilarity model. The periodicity in the temporal variation of acoustic scenes is first pointed out and then used to build a new stochastic model. In the new model, the temporal variation is expressed by dissimilarity be…
This paper proposes a new non-negative matrix factorization (NMF) based acoustic event detection (AED) method with mixtures of local dictionaries (MLD) and activation aggregation. One of the key problems of conventional NMF-based methods is instability of activations due to redundancy of a region sp…