28 accepted papers
Time stretching of music signals has a crucial problem, i.e., smearing of percussive sounds. Some time stretching algorithms have addressed this problem by detecting percussive components and manipulating them differently from the other components. However, conventional methods cause artifacts. In t…
In this paper, we propose a novel audio declipping method that fuses sparse-optimization-based and deep neural network (DNN)– based methods. The two methods have contrasting characteristics, depending on clipping level. Sparse-optimization-based audio de-clipping can preserve reliable samples, being…
In this paper, we propose an audio declipping method that takes advantages of both sparse optimization and deep learning. Since sparsity-based audio declipping methods have been developed upon constrained optimization, they are adjustable and well-studied in theory. However, they always uniformly pr…
Phase reconstruction from amplitude spectrograms has attracted attention in recent acoustics because of its potential applications in speech synthesis and enhancement. The most well-known algorithm in acoustics is based on alternating projection and called Griffin– Lim algorithm (GLA). At the same t…
Harmonic and percussive sound separation (HPSS) is a widely applied pre-processing tool that extracts distinct (harmonic and percussive) components of a signal. In the previous methods, HPSS has been performed based on the structural properties of magnitude (or power) spectrograms. However, such app…
Sound event localization and detection (SELD) is a combined task of identifying the sound event and its direction. Deep neural networks (DNNs) are utilized to associate them with the sound signals observed by a microphone array. Although ambisonic microphones are popular in the literature of SELD, t…
Nonstationary signals are commonly analyzed and processed in the time-frequency (T-F) domain that is obtained by the discrete Gabor transform (DGT). The T-F representation obtained by DGT is spread due to windowing, which may degrade the performance of T-F domain analysis and processing. To obtain a…
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…
The short-time Fourier transform (STFT) is widely employed in non-stationary signal analysis, whose property depends on window functions. Instantaneous frequency in STFT, the time-derivative of phase, is recently applied to many applications including spectrogram reassignment. The computation of ins…
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…
Detecting sound source objects within visual observation is important for autonomous robots to comprehend surrounding environments. Since sounding objects have a large variety with different appearances in our living environments, labeling all sounding objects is impossible in practice. This calls f…
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…
Recent development of optical interferometry enables us to measure sound without placing any device inside the sound field. In particular, parallel phase-shifting interferometry (PPSI) has realized advanced measurement of refractive index of air. Its novel application investigated very recently is s…
Low-rankness of amplitude spectrograms has been effectively utilized in audio signal processing methods including non-negative matrix factorization. However, such methods have a fundamental limitation owing to their amplitude-only treatment where the phase of the observed signal is utilized for resy…
Decomposition of an audio mixture into harmonic and percussive components, namely harmonic/percussive source separation (HPSS), is a useful pre-processing tool for many audio applications. Popular approaches to HPSS exploit the distinctive source-specific structures of power spectrograms. However, s…
Estimating envelope of a signal has various applications including empirical mode decomposition (EMD) in which the cubic C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -spline based envelope estimation is generally used. While such functional…
A parametric array loudspeaker (PAL) consists of a lot of ultrasonic transducers in most cases and is driven by an ultrasonic which is modulated by audible sound. Because each ultrasonic transducer has each difference resonant frequency, there is the individual difference in ultrasonic transducers o…
For a musical instrument sound containing partials, or modes, the behavior of modes around the attack time is particularly important. However, accurately decomposing it around the attack time is not an easy task, especially when the onset is sharp. This is because spectra of the modes are peaky whil…
The piano is one of the most popular and attractive musical instruments that leads to a lot of research on it. To synthesize the piano sound in a computer, many modeling methods have been proposed from full physical models to approximated models. The focus of this paper is on the latter, approximati…
In optimization-based signal processing, the so-called prior term models the desired signal, and therefore its design is the key factor to achieve a good performance. For audio signals, the time-directional total variation applied to a spectrogram in combination with phase correction has been propos…
Wave-based acoustic simulation methods are studied actively for predicting acoustical phenomena. Finite-difference time-domain (FDTD) method is one of the most popular methods owing to its straightforwardness of calculating an impulse response. In an FDTD simulation, an omnidirectional sound source…
In this paper, 3D sound source localization method for simultaneously estimating both direction-of-arrival (DOA) and distance from the microphone array is proposed. For estimating distance, the off-grid problem must be overcome because the range of distance to be considered is quite broad and even n…
Nowadays, various types of microphone array are used in many applications. However, it is not easy to compare arrays of different types because each array has been treated by a specific theory depending on the type of an array. Although several criteria have been proposed for microphone arrays for e…
Recent development of microphone arrays which consist of more than several tens or hundreds microphones enables acquisition of rich spatial information of sound. Although such information possibly improve performance of any array signal processing technique, the amount of data will increase as the n…
Visualization is an effective way to understand the behavior of a sound field. There are several methods for such observation including optical measurement technique which enables a non-destructive acoustical observation by detecting density variation of the medium. For audible sound propagating thr…
Visualization of sound field using Schlieren technique provides many advantages. It enables us to investigate the change of the sound field in real-time from every point of the observing region. However, since the density gradient of air caused by the disturbance of acoustic field is very small, it…