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We propose a method for optimizing an acoustic feature extractor for anomalous sound detection (ASD). Most ASD systems adopt outlier-detection techniques because it is difficult to collect a massive amount of anomalous sound data. To improve the performance of such outlier-detection-based ASD, it is essential to extract a set of efficient acoustic features that is suitable for identifying anomalous...
An informative acoustic-feature-selection method for collecting target sources in noisy environments is proposed. Wiener filtering is a powerful framework for sound-source enhancement. For Wiener-filter estimation, statistical-mapping functions, such as deep neural network based or Gaussian mixture model based mappings, have been used. In this framework, it is essential to find informative acoustic...
We investigated whether a deep neural network (DNN)-based source enhancement function can be self-optimized by reinforcement learning (RL). The use of a DNN is a powerful approach to describing the relationship between two sets of variables and can be useful for source enhancement function design. By training the DNN using a huge amount of training data, sound quality of output signals are improved...
A method for constructing deep neural networks (DNNs) for accurate supervised source enhancement is proposed. Attempts were made in previous studies to estimate the power spectral densities (PSDs) of sound sources, which are used to estimate Wiener filters for source enhancement, from the output of multiple beamformings using DNNs. Although performance improved, it was not possible to guarantee accurate...
The relationships between the amplitude and phase of the short-time Fourier transform (STFT) are investigated. By choosing the Gaussian window for the STFT, we reveal that the group delay and instantaneous frequency of each signal segment, both of which are derived from the phase by definition, can also be explicitly linked with the amplitude. As a result, the amplitude and phase can also be linked...
We propose a method for estimating the prior signal-to-noise ratio (SNR), which is used for calculating the Wiener filter for distant sound source extraction, from output signals of beamforming using statistical mapping based on the deep neural network (DNN). Since informative features to estimate the prior SNR are included in multiple beamforming outputs, the SNR can be accurately estimated by this...
We investigated informative acoustic feature extraction based on dimension reduction for collecting target sources on a noisy sports field. Although a Wiener filter is often used for sound source enhancement, it is difficult to accurately design the Wiener filter by simply using spatial cues because the noise on a sports field (e.g., cheering from spectators) arrives from the same direction as that...
We propose a Wiener filter design method for collecting target sources on a noisy sports field. Because the noise on a sports field, e.g., cheering from the audience, arrives from the same direction as that of the targeted source, it is difficult to accurately design a Wiener filter by simply using spatial cues. This study focused on a combination of spatial cues and acoustic feature modeling. The...
A brain-computer interface (BCI) character input experiment focusing on participants’ BCI intelligibility was performed. In theory, a BCI can be operated by anyone if cognitive activity is possible. However, individual differences clearly occur in practice. Therefore, we supposed that this difference was related to BCI intelligibility. In a previous study, BCI experts and BCI novice users were compared...
This paper presents an intra-note segmentation method for mono-phonic recordings based on acoustic feature variation; each musical note is separated into onset, steady and offset states. The task of intra-note segmentation from audio signals is detecting change points of acoustic feature. In proposed method, the Markov process is assumed on state transition, and time-varying acoustic feature is represented...
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