22 accepted papers
We propose an end-to-end speech enhancement method with trainable time-frequency (T-F) transform based on invertible deep neural network (DNN). The resent development of speech enhancement is brought by using DNN. The ordinary DNN-based speech enhancement employs T-F transform, typically the short-t…
Phase reconstruction, which estimates phase from a given amplitude spectrogram, is an active research field in acoustical signal processing with many applications including audio synthesis. To take advantage of rich knowledge from data, several studies presented deep neural network (DNN)–based phase…
We propose a speech enhancement method using a causal deep neural network (DNN) for real-time applications. DNN has been widely used for estimating a time-frequency (T-F) mask which enhances a speech signal. One popular DNN structure for that is a recurrent neural network (RNN) owing to its capabili…
We propose a similarity function for one-shot anomaly detection in sounds (ADS) called SPecific anomaly IDentifiER network (SPIDERnet). In ADS systems, since overlooking an anomaly may result in serious incidents, we need to update such systems using an (often only one) overlooked anomalous sample.…
Sound event detection (SED) and acoustic scene classification (ASC) are major tasks in environmental sound analysis. Considering that sound events and scenes are closely related to each other, some works have addressed joint analyses of sound events and acoustic scenes based on multitask learning (M…
We propose a direction-of-arrival (DOA) estimation method for Sound Event Localization and Detection (SELD). Direct estimation of DOA using a deep neural network (DNN), i.e. completely-datadriven approach, achieves high accuracy. However, there is a gap in the accuracy between DOA estimation for sin…
This paper investigates a self-adaptation method for speech enhancement using auxiliary speaker-aware features; we extract a speaker representation used for adaptation directly from the test utterance. Conventional studies of deep neural network (DNN)-based speech enhancement mainly focus on buildin…
Improving subjective sound quality of enhanced signals is one of the most important missions in speech enhancement. For evaluating the subjective quality, several methods related to perceptually-motivated objective sound quality assessment (OSQA) have been proposed such as PESQ (perceptual evaluatio…
Supervised anomaly detection has been a tough problem due to its necessity of special handling of unseen anomalies. In this paper, we present a heuristic implementation of variational auto-encoder with von-Mises Fisher prior applied to a supervised anomaly detector. The closed latent space like sphe…
We tackle unsupervised anomaly detection (UAD), a problem of detecting data that significantly differ from normal data. UAD is typically solved by using density estimation. Recently, deep neural network (DNN)-based density estimators, such as Normalizing Flows, have been attracting attention. Howeve…
We propose a data-driven design method of perfect-reconstruction filterbank (PRFB) for sound-source enhancement (SSE) based on deep neural network (DNN). DNNs have been used to estimate a time-frequency (T-F) mask in the short-time Fourier transform (STFT) domain. Their training is more stable when…
This paper presents a novel phase reconstruction method (only from a given amplitude spectrogram) by combining a signal-processing-based approach and a deep neural network (DNN). To retrieve a time-domain signal from its amplitude spectrogram, the corresponding phase is required. One of the popular…
In anomaly detection systems, overlooking anomalies may result in serious incidents. Thus, when a system overlooks an anomaly, we need to update the system to never overlook the observed type of anomalies twice. There are roughly two possible approaches to solve this problem; re-training the whole s…
This study proposes a trainable adaptive window switching (AWS) method and apply it to a deep-neural-network (DNN) for speech enhancement in the modified discrete cosine transform domain. Time-frequency (T-F) mask processing in the short-time Fourier transform (STFT)-domain is a typical speech enhan…
Anomalies have broad patterns corresponding to their causes. In industry, anomalies are typically observed as equipment failures. Anomaly detection aims to detect such failures as anomalies. Although this is usually a binary classification task, the potential existence of unseen (unknown) failures m…
This paper presents an end-to-end deep neural network (DNN)-based source enhancement on the basis of a time-frequency (T-F) mask processing in the modified discrete cosine transform (MDCT)-domain. To retrieve the target signal perfectly in the discrete Fourier transform (DFT)-domain, both amplitude…
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. B…
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…
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…
Web applications for watching omnidirectional video through head-mounted displays (HMDs) or smartphones have been widely distributed. The goal of this study was to generate binaural sounds corresponding to the user viewpoint. Assuming that a microphone array is used for sound recording, the enhanced…
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…
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 t…