Improvement of DOA Estimation by using Quaternion Output in Sound Event Localization and Detection

This paper describes improvement of Direction of Arrival (DOA) estimation performance using quaternion output in the Detection and Classification of Acoustic Scenes and Events (DCASE) 2019 Task 3. DCASE 2019 Task3 focuses on the sound event localization and detection (SELD) which is a task that simultaneously estimates the sound source direction in addition to conventional sound event detection (SED). In the baseline method, the sound source direction angle is directly regressed. However, the angle is a periodic function and it has discontinuities which may make learning unstable. Specifical-ly, even though -180 deg and 180 deg are in the same direc-tion, a large loss is calculated. Estimating DOA angles with a classification approach instead of regression can solve such instability of discontinuities but this causes limitation of reso-lution. In this paper, we propose to introduce the quaternion which is a continuous function into the output layer of the neural network instead of directly estimating the sound source direction angle. This method can be easily implemented only by changing the output of the existing neural network, and thus does not significantly increase the number of parameters in the middle layers. Experimental results show that proposed method improves the DOA estimation without significantly increasing the number of parameters.24424

Nonparametric Bayesian Dereverberation of Power Spectrograms Based on Infinite-Order Autoregressive Processes

This paper describes a monaural audio dereverberation method that operates in the power spectrogram domain. The method is robust to different kinds of source signals such as speech or music. Moreover, it requires little manual intervention, including the complexity of room acoustics. The method is based on a non-conjugate Bayesian model of the power spectrogram. It extends the idea of multi-channel linear prediction to the power spectrogram domain, and formulates a model of reverberation as a non-negative, infinite-order autoregressive process. To this end, the power spectrogram is interpreted as a histogram count data, which allows a nonparametric Bayesian model to be used as the prior for the autoregressive process, allowing the effective number of active components to grow, without bound, with the complexity of data. In order to determine the marginal posterior distribution, a convergent algorithm, inspired by the variational Bayes method, is formulated. It employs the minorization-maximization technique to arrive at an iterative, convergent algorithm that approximates the marginal posterior distribution. Both objective and subjective evaluations show advantage over other methods based on the power spectrum. We also apply the method to a music information retrieval task and demonstrate its effectiveness

Mechanisms of Neuronal Death in Synucleinopathy

α-synuclein is a key molecule in the pathogenesis of synucleinopathy including Parkinson's disease and multiple system atrophy. In this mini-review, we mainly focus on recent data obtained from cellular models of synucleinopathy and discuss the possible mechanisms of neurodegeneration. Recent progress suggests that the aggregate formation of α-synuclein is cytoprotective and that its precursor oligomer (protofibril) may be cytotoxic. The catechol-derived quinones are the candidate molecules that facilitate the oligomer formation of α-synuclein. Furthermore, the cellular membranes are shown to be the primary targets injured by mutant α-synucleins, and the mitochondrial dysfunction seems to be an initial step in the neuronal death

Noise robust 2D bird localization via sound using microphone arrays

Birds in the wild are difficult to localize, because their sizes tend to be small, they move swiftly, and they are often visually occluded. However, their location information is crucial for ethological studies on birds' behaviour. Recently, automating the process has been studied as a hot topic, where spatial sensors and sensor networks are commonly used. To avoid the visual occlusion problem, many studies focus on acoustic signal processing by applying microphone arrays and perform 1D azimuth localization through bird songs. In this study, we perform 2D sound source localization in the Cartesian coordinates using azimuths from multiple microphone arrays. To estimate the exact bird's location, we calculate the intersection points of these azimuth lines. Although this approach is simple and easy to be implemented, it has two main issues. One is that even small noise interference in azimuth values results in corrupting the localization data. This leads to a problem, where the intersection points between the azimuth lines do not intersect in one point for a single bird, but in several points. This proves difficulty in estimating the exact location of each bird. Especially in a far-field application, even small noise corruption leads to large localization errors. The other issue is that in the bird's natural habitat, elements such as leaves, grass and rivers are natural noise sources. It is difficult to extract the bird songs in such a noisy environment. We propose an algorithm involving statistic methods, sound feature analysis and machine learning. Based on this approach, a noise robust bird localization system has been established. We have performed numerous simulations to further understand the limitations of the system. Based on the results we have also derived the system's design guidelines, describing how the results change depending on the number of microphone arrays, signal-to-noise ratio, bird's distance from the devices, array's transfer function, type of the singing bird and specific parameter settings used in the algorithms. Such detailed guidelines support interested researchers in creating a similar system, which can contribute to ethological researches

The AAA-ATPase VPS4 Regulates Extracellular Secretion and Lysosomal Targeting of α-Synuclein

Many neurodegenerative diseases share a common pathological feature: the deposition of amyloid-like fibrils composed of misfolded proteins. Emerging evidence suggests that these proteins may spread from cell-to-cell and encourage the propagation of neurodegeneration in a prion-like manner. Here, we demonstrated that α-synuclein (αSYN), a principal culprit for Lewy pathology in Parkinson's disease (PD), was present in endosomal compartments and detectably secreted into the extracellular milieu. Unlike prion protein, extracellular αSYN was mainly recovered in the supernatant fraction rather than in exosome-containing pellets from the neuronal culture medium and cerebrospinal fluid. Surprisingly, impaired biogenesis of multivesicular body (MVB), an organelle from which exosomes are derived, by dominant-negative mutant vacuolar protein sorting 4 (VPS4) not only interfered with lysosomal targeting of αSYN but facilitated αSYN secretion. The hypersecretion of αSYN in VPS4-defective cells was efficiently restored by the functional disruption of recycling endosome regulator Rab11a. Furthermore, both brainstem and cortical Lewy bodies in PD were found to be immunoreactive for VPS4. Thus, VPS4, a master regulator of MVB sorting, may serve as a determinant of lysosomal targeting or extracellular secretion of αSYN and thereby contribute to the intercellular propagation of Lewy pathology in PD

多重奏音楽音響信号に対する楽器音の分離とその応用

京都大学0048新制・課程博士博士(情報学)甲第16201号情博第406号新制||情||74(附属図書館)28780京都大学大学院情報学研究科知能情報学専攻(主査)教授 奥乃 博, 教授 河原 達也, 教授 田中 利幸学位規則第4条第1項該当Doctor of InformaticsKyoto UniversityDA

Parameter Estimation of Virtual Musical Instrument Synthesizers

(Abstract to follow