Takafumi Hikichi

Precise Dereverberation Using Multichannel Linear Prediction

In this paper, we discuss the numerical problems posed by the previously reported LInear-predictive Multi-input Equalization (LIME) algorithm when dealing with dereverberation of long room transfer functions (RTF). The LIME algorithm consists of two steps. First, a speech residual is calculated using multichannel linear prediction. The residual is free from the room reverberation effect but it is also excessively whitened because the average speech characteristics have been removed. In the second step, LIME estimates such average speech characteristics to compensate for the excessive whitening. When multiple microphones are used, the speech characteristics are common to all microphones whereas the room reverberation differs for each microphone. LIME estimates the average speech characteristics as the characteristics that are common to all the microphones. Therefore, LIME relies on the hypothesis that there are no zeros common to all channels. However, it is known that RTFs have a large number of zeros close to the unit circle on the z-plane. Consequently, the zeros of the RTFs are distributed in the same regions of the z-plane and, if an insufficient number of microphones are used, the channels would present numerically overlapping zeros. In such a case, the dereverberation algorithm would perform poorly. We discuss the influence of overlapping zeros on the dereverberation performance of LIME. Spatial information can be used to deal with the problem of overlapping zeros. By increasing the number of microphones, the number of overlapping zeros decreases and the dereverberation performance is improved. We also examine the use of cepstral mean normalization for post-processing to reduce the remaining distortions caused by the overlapping zeros

Dereverberation and Denoising Using Multichannel Linear Prediction

Reverberation in a room severely degrades the characteristics and auditory quality of speech captured by distant microphones, thus posing a severe problem for many speech applications. Several dereverberation techniques have been proposed with a view to solving this problem. There are, however, few reports of dereverberation methods working under noisy conditions. In this paper, we propose an extension of a dereverberation algorithm based on multichannel linear prediction that achieves both the dereverberation and noise reduction of speech in an acoustic environment with a colored noise source. The method consists of two steps. First, the speech residual is estimated from the observed signals by employing multichannel linear prediction. When we use a microphone array, and assume, roughly speaking, that one of the microphones is closer to the speaker than the noise source, the speech residual is unaffected by the room reverberation or the noise. However, the residual is degraded because linear prediction removes an average of the speech characteristics. In a second step, the average of the speech characteristics is estimated and used to recover the speech. Simulations were conducted for a reverberation time of 0.5 s and an input signal-to-noise ratio of 0 dB. With the proposed method, the reverberation was suppressed by more than 20 dB and the noise level reduced to -18 dB.

Blind dereverberation based on estimates of signal transmission channels without precise information on channel order [speech processing applications]

This paper addresses the blind dereverberation problem of single-input multiple-output acoustic systems. Most approaches require an exact knowledge of the order of the room transfer functions. In this paper, we propose an equalization algorithm that is less sensitive to the order of the estimated transfer functions. First, the transfer functions are estimated using an overestimated order, and the inverse filter set for this estimated transfer functions is calculated. Since the estimated transfer functions have a common part, the signal processed by the inverse filter set contains distortion. Then, we compensate for this distortion using a common polynomial extraction technique. This algorithm enables a reverberated speech signal to be dereverberated as long as the channel is overestimated. Simulation results show that the proposed method is robust even when the order is highly overestimated.

On the Use of Lime Dereverberation Algorithm in an Acoustic Environment With a Noise Source

This paper addresses the speech dereverberation problem in the presence of a noise source. We show that the previously presented linear-predictive multi-input equalization (LIME) algorithm can achieve both dereverberation and noise reduction. Experiments show that, for a reverberation time of 0.2 seconds, precise dereverberation is possible in the presence of a colored noise source of SNR of 5 dB, with a dereverberation of the room impulse response by more than 20 dB

Study on Speech Dereverberation with Autocorrelation Codebook

This paper proposes a new speech dereverberation approach based on a statistical speech model. An autocorrelation codebook is introduced as a model that can represent time-varying short-time speech characteristics corresponding to the cepstrum and harmonics. The speech dereverberation is formulated as a likelihood maximization problem, in which the quality of a speech signal is recovered by turning the signal into one that is probabilistically more like a clean speech. Two dereverberation algorithms are derived based on different scenarios, regularized inversion and inverse filter estimation. Experimental results show that the proposed approach allows us to reduce both reverberation and noise with the regularized inversion, and to estimate inverse filters that can dereverberate signals effectively from just a small number of observed signals.

Maximum likelihood approach to speech enhancement for noisy reverberant signals

This paper proposes a speech enhancement method for signals contaminated by room reverberation and additive background noise. The following conditions are assumed: (1) The spectral components of speech and noise are statistically independent Gaussian random variables. (2) The convolutive distortion channel is modeled as an auto-regressive system in each frequency bin. (3) The power spectral density of speech is modeled as an all-pole spectrum, while that of noise is assumed to be stationary and given in advance. Under these conditions, the proposed method estimates the parameters of the channel and those of the all-pole speech model based on the maximum likelihood estimation method. Experimental results showed that the proposed method successfully suppressed the reverberation and additive noise from three-second noisy reverberant signals when the reverberation time was 0.5 seconds and the reverberant signal to noise ratio was 10 dB.

Robust blind dereverberation of speech signals based on characteristics of short-time speech segments

This paper addresses blind dereverberation techniques based on the inherent characteristics of speech signals. Two challenging issues for speech dereverberation involve decomposing reverberant observed signals into colored sources and room transfer functions (RTFs), and making the inverse filtering robust as regards acoustic and system noise. We show that short-time speech characteristics are very important for this task, and that multi-channel linear prediction (MCLP) is a useful tool for achieving robust inverse filtering. As examples, we detail three recently proposed robust dereverberation methods. By assuming the source to be a small order autoregressive process, we can present an efficient source estimation method that reduces late reverberation reflections of the reverberation using multi-step linear prediction. By exploiting the time-varying nature of the speech signals, we can also develop a method that can estimate both the source and the inverse filters of the RTFs. Furthermore, we can achieve high quality speech dereverberation by formulating the problem as a likelihood maximization problem using a statistical speech model that represents the spectral characteristics of short-time speech segments including harmonicity.

Sho-So-In: Control of a Physical Model of the Sho by Means of Automatic Feature Extraction from Real Sounds

This paper proposes a synthesis framework for sound hybridization that creates sho-like sounds with articulations that are the same as that of a given input signal. This approach has three components: acoustic feature extraction, physical parameter estimation, and waveform synthesis. During acoustic feature extraction, the amplitude and fundamental frequency of the input signal are extracted, and in the parameter estimation stage these values are converted to control parameters for the physical model. Then, using these control parameters, a sound waveform is calculated during the synthesis stage. Based on the proposed method, a mapping function between acoustical parameters and physical parameters was determined using recorded sho sounds. Then, sounds with various articulations were synthesized using several kinds of instrumental tones. As a result, sounds with natural frequency and amplitude variations such as vibrato and portamento were created. The proposed method was used in music composition and proved to be effective.

Inverse Filtering for Speech Dereverberation Without the Use of Room Acoustics Information

This chapter discusses multi-microphone inverse filtering, which does not use a priori information of room acoustics, such as room impulse responses between the target speaker and the microphones. One major problem as regards achieving this type of processing is the degradation of the recovered speech caused by excessive equalization of the speech characteristics. To overcome this problem, several approaches have been studied based on a multichannel linear prediction framework, since the framework may be able to perform speech dereverberation as well as noise attenuation. Here, we first discuss the relationship between optimal filtering and linear prediction. Then, we review our four approaches, which differ in terms of their treatment of the statistical properties of a speech signal.

On a Blind Speech Dereverberation Algorithm Using Multi-Channel Linear Prediction

It is well known that speech captured in a room by distant microphones suffers from distortions caused by reverberation. These distortions may seriously damage both speech characteristics and intelligibility, and consequently be harmful to many speech applications. To solve this problem, we proposed a dereverberation algorithm based on multi-channel linear prediction. The method is as follows. First we calculate prediction filters that cancel out the room reverberation but also degrade speech characteristics by causing excessive whitening of the speech. Then, we evaluate the prediction-filter degradation to compensate for the excessive whitening. As the reverberation lengthens, the compensation performance becomes worse due to computational accuracy problems. In this paper, we propose a new computation that may improve compensation accuracy when dealing with long reverberation.