This paper presents a statistical method of single-channel speech enhancement that uses a variational autoencoder (VAE) as a prior distribution on clean speech. A standard approach to speech enhancement is to train a deep neural network (DNN) to take noisy speech as input and output clean speech. Although this supervised approach requires a very large amount of pair data for training, it is not robust against unknown environments. Another approach is to use nonnegative matrix factorization (NMF) based on basis spectra trained on clean speech in advance and those adapted to noise on the fly. This semi-supervised approach, however, causes considerable signal distortion in enhanced speech due to the unrealistic assumption that speech spectrograms are linear combinations of the basis spectra. Replacing the poor linear generative model of clean speech in NMF with a VAE-a powerful nonlinear deep generative modeltrained on clean speech, we formulate a unified probabilistic generative model of noisy speech. Given noisy speech as observed data, we can sample clean speech from its posterior distribution. The proposed method outperformed the conventional DNN-based method in unseen noisy environments.

Abstract-A method has been developed for improving sound source localization (SSL) using a microphone array from an unmanned aerial vehicle with multiple rotors, a "multirotor UAV". One of the main problems in SSL from a multirotor UAV is that the ego noise of the rotors on the UAV interferes with the audio observation and degrades the SSL performance. We employ a generalized eigenvalue decomposition-based multiple signal classification (GEVD-MUSIC) algorithm to reduce the effect of ego noise. While GEVD-MUSIC algorithm requires a noise correlation matrix corresponding to the auto-correlation of the multichannel observation of the rotor noise, the noise correlation is nonstationary due to the aerodynamic control of the UAV. Therefore, we need an adaptive estimation method of the noise correlation matrix for a robust SSL using GEVD-MUSIC algorithm. Our method uses a Gaussian process regression to estimate the noise correlation matrix in each time period from the measurements of self-monitoring sensors attached to the UAV such as the pitch-roll-yaw tilt angles, xyz speeds, and motor control values. Experiments compare our method with existing SSL methods in terms of precision and recall rates of SSL. The results demonstrate that our method outperforms existing methods, especially under high signal-to-noise-ratio conditions.

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This paper describes multichannel speech enhancement for improving automatic speech recognition (ASR) in noisy environments. Recently, the minimum variance distortionless response (MVDR) beamforming has widely been used because it works well if the steering vector of speech and the spatial covariance matrix (SCM) of noise are given. To estimating such spatial information, conventional studies take a supervised approach that classifies each time-frequency (TF) bin into noise or speech by training a deep neural network (DNN). The performance of ASR, however, is degraded in an unknown noisy environment. To solve this problem, we take an unsupervised approach that decomposes each TF bin into the sum of speech and noise by using multichannel nonnegative matrix factorization (MNMF). This enables us to accurately estimate the SCMs of speech and noise not from observed noisy mixtures but from separated speech and noise components. In this paper we propose online MVDR beamforming by effectively initializing and incrementally updating the parameters of MNMF. Another main contribution is to comprehensively investigate the performances of ASR obtained by various types of spatial filters, i.e., time-invariant and variant versions of MVDR beamformers and those of rank-1 and fullrank multichannel Wiener filters, in combination with MNMF. The experimental results showed that the proposed method outperformed the state-of-the-art DNN-based beamforming method in unknown environments that did not match training data.

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