The harmonic distortion level (HDL), an important criterion used to evaluate loudspeaker performance, can usually be measured using spectral analysis. However, spectral analysis entails great computational complexity. Furthermore, long measurement times are necessary to measure HDL accurately in a noisy environment. Therefore, we proposed the HDL measurement technique for a loudspeaker using an adaptive filter. This measurement technique can measure HDL as accurately as spectral analysis using fast Fourier transform (FFT). Furthermore, its computational complexity is much less than that of spectral analysis. Nevertheless, this measurement technique requires a long measurement time to assess the convergence of filter coefficients. This paper presents a description of the new HDL measurement technique of a loudspeaker using plural adaptive filters. The proposed measurement technique can measure HDL more accurately than spectral analysis in a noisy environment. We evaluated the performance of the proposed measurement technique using computer simulations. Results based on computer simulations show that the proposed measurement technique is more effective than spectral analysis in an acoustic environment with background noise.

This paper addresses the problem of direction-of-arrival (DOA) estimation both in azimuthal and elevation angle from binaural sound that is processed with a head-related transfer function (HRTF). Previously, we proposed a weighted Wiener gain (WWG) method for two-dimensional DOA estimation with two-directional microphones. However, for signals processed with HRTFs, peaks in the spatial spectra of WWG indicating true sources can mingle with spurious peaks. To resolve this situation, we propose to apply incremental source attenuation (ISA) in combination with WWG. In fact, ISA reduces spectral components originating from specified sound sources and thereby improves the localization accuracy of the next targeted source in the proposed incremental estimation procedure. We conduct computer simulations using directional microphones and four HRTF sets corresponding to four individuals. The proposed method is compared to two DOA estimation methods that are equivalent to two generalized cross-correlation functions and two high-resolution methods of multiple signal classification (MUSIC) and minimum variance method. For comparison purposes, we introduce binary coherence detection (BCD) to high-resolution methods for emphasizing valid spectral components for localization in multiple source conditions. Evaluation results demonstrate that, although MUSIC with BCD yield comparable performance to that of WWG in conditions where single speech source exists, WWG with ISA surpasses the other methods in conditions including two or three speech sources.

We propose a new method for estimating directions of arrival (DOAs) of sound sources, both in azimuthal and elevation angle, using two directional microphones. This method adopts weighted Wiener gain (WWG) for DOA estimation. WWG is an estimate of the Wiener gain that we proposed for use in automatic gain control to enhance speech that is degraded by additive noise. Angular resolution of WWG arises from spectral subtraction (SS)-based noise reduction involved in the WWG calculation, which enhances the signal from the look direction while suppressing signals from other directions. Because WWG involves two-channel SS, which can deal with instantaneous noise, noise sources need not to be stationary, as they must be with ordinary single-channel SS. We further propose the exploitation of a pair of directional microphones whose front directions are arranged in rotational symmetry. The time difference and amplitude difference between the two-channel signal provided by the microphones are utilized to yield a two-dimensional resolution of DOA. We evaluated the proposed method through computer simulations and compared it to three DOA estimation methods that are based on a cross-correlation function and two popular high-resolution methods of multiple signal classification and minimum variance method. Evaluation results of the source detection rate and estimation accuracy demonstrate the remarkable superiority of our method compared to the other methods in conditions where multiple speech sources exist

We propose a new method of speech enhancement based on auto gain control (AGC) using two channel inputs to deal with transient noises. Auto gain control is considered to be relatively ineffective for reducing noises that are superimposed on speech. Nevertheless, it offers advantages for addressing problems posed by musical noise and spectral distortion. This method combines two operations for obtaining accurate gain. One is spectral subtraction for two-channel input (2chSS); the other is self-offset of the noise with pre-whitening. This study also addresses a coherence based post-filter to reduce uncorrelated noise components among channels. The proposed method is evaluated in experiments across three noise conditions in which (i) impulsive noises, (ii) stationary car noise, and (iii) speech noise are present, respectively. Objective measures and spectrograms demonstrate marked improvements over other two-microphone based methods, but subjective preference tests reveal that the proposed method is less preferred than the equivalent of a nonprocessed signal in the case of stationary car noise (ii). The performance of the proposed method and the conventional 2chSS were even in the case of speech noise (iii). These results of subjective tests reflect some disadvantages of the AGC processing. Those drawbacks involve degradation of noise consistency in stationary noise conditions and residual noises in desired speech segments. Nevertheless, subjective tests in the case of noise (i) demonstrate that the proposed method is the most preferred among the methods compared here. The effectiveness of the proposed method is confirmed particularly for this noise condition.

We propose a new method for implementing Karhunen–Loeve transform (KLT)-based speech enhancement to exploit vector quantization (VQ). The method is suitable for real-time processing. The proposed method consists of a VQ learning stage and a filtering stage. In the VQ learning stage, the autocorrelation vectors comprising the first$K$elements of the autocorrelation function are extracted from learning data. The autocorrelation vectors are used as codewords in the VQ codebook. Next, the KLT bases that correspond to all the codeword vectors are estimated through eigendecomposition (ED) of the empirical Toeplitz covariance matrices constructed from the codeword vectors. In the filtering stage, the autocorrelation vectors that are estimated from the input signal are compared to the codewords. The nearest one is chosen in each frame. The precomputed KLT bases corresponding to the chosen codeword are used for filtering instead of performing ED, which is computationally intensive. Speech quality evaluation using objective measures shows that the proposed method is comparable to a conventional KLT-based method that performs ED in the filtering process. Results of subjective tests also support this result. In addition, processing time is reduced to about 1/66 that of the conventional method in the case where a frame length of 120 points is used. This complexity reduction is attained after the computational cost in the learning stage and a corresponding increase in the associated memory requirement. Nevertheless, these results demonstrate that the proposed method reduces computational complexity while maintaining the speech quality of the KLT-based speech enhancement.

This paper proposes a method to estimate the waveform of sound sources. This is accomplished by using a microphone array where the microphones are not situated close to the sound sources. The waveform is estimated using the sound velocity and positions of the microphones and sound sources as the only fixed parameters. The positions of the sound sources can be estimated in advance by using a conventional method such as multiple signal classification method (MUSIC). An iteration method is introduced to reduce the effect of direct sounds and of significant image sources due to reflected noise and/or other sound sources, whose positions are known. Computer simulations and an experiment with loudspeakers were conducted to demonstrate the effectiveness of our method.

A method is described to estimate, using many sensors (microphones) both the position and the waveform of individual sound sources by decreasing the effects of other sound sources and reflection from walls and/or ceilings. Some experiments using two sound sources and 16 sensors have been carried out in a room with a reverberation time of 0.5 s, and it is found that the waveform of the specified sound source could be estimated by decreasing the effect of other sound sources by about 20 dB.>

A method to estimate the shape of the foundation piles is described. The head of the pile is hit with a small hammer and the reflected wave is recorded with a vibration pick-up attached to the same head. The method automatically estimates the shape of the pile from the recorded wave. By computer simulations and experiments using model piles, it is confirmed that the proposed method correctly estimates the shape of the underground piles.>

This paper describes a new method to locate sound sources using many sensors. Spectra of all the sensor outputs are calculated by discrete Fourier transform. In the proposed method, following parameters are assumed; (1) the number of sound sources, (2) a position of each sound source, and (3) the spectrum of the O-th sensor output due to each sound source. The spectra of all the sensor outputs are estimated using the parameters under the assumption of free field. Then, introduced is an error function which is the mean-square value of the difference between the calculated spectra and the estimated ones. By changing the values of the parameters, the minimum value of the error function is searched. When the error function takes the minimum value, the parameters represent the estimates; the number of sound sources, and the positions and spectra of the sound sources. By inverse Fourier transforming the estimated spectra, the wave form of a specified sound source can be obtained.

This paper describes a new method to estimate accurately the parameters of an auto-regressive (AR) process corrupted with high level white noise. It is based on the phase matching by minimizing the difference between the phase of the all-zero model and the phase of the maximum phase signal reconstructed from the power spectrum of the observed signal. The parameters of the AR model are obtained from the finite length sequence of the estimated all-zero model. Since the phase matching technique is satisfactory to the conditions needed in applying the least mean square method, the AR parameters are estimated accurately even at the low signal-to-noise ratio. The iterative or non-iterative methods in the recent literature fail in reconstructing the all-zero model from the power spectrum, when there are dips and peaks having no correlation with the poles of original AR signal in the power spectrum. However, the method proposed in this paper is also used for reconstructing the phase from the power spectrum in such cases. Finally, it is confirmed with computer simulations that the proposed method is useful for accurate estimation of the AR parameters.

This paper describes the method to find out the outbreak of a sound source and to estimate the position of the source in noisy environment using distributed microphones. A source point is first assumed and the cross spectrum between the outputs of every pair of microphones is computed after compensating the spectral change by transmission path from an assumed source point to the microphones. Then the cross spectra are averaged. Scanning the assumed point, the averaged cross spectrum is computed for every assumed point. And the source point is estimated as the point of maximum magnitude of the averaged cross spectrum. The outbreak of a sound source is detected by the change in the averaged cross spectra. It is a practical problem that a number of computation of FFT is necessary in the proposed method, and this problem is solved by developing a high speed pipeline processor. The effectiveness of the method is confirmed by the computer simulation and by the experiments.

We have already reported that composite complex sinusoidal modeling (CCSM) is effective for array signal processing, in the sense that highest bearing resolution was achieved by CCSM than by any other existing methods time. The directivity pattern obtained by CCSM has no side lobe and gives only lines indicating the directions and the powers of incident plane waves at a concerning frequency. We propose here an improved CCSM to achieve a much more bearing estimation.

This paper describes a new method of estimating spectrum of incident sound from a specified direction. A fixed microphone and a moving microphone form a basic composition. The moving microphone moves along a straight line from the fixed one to the specified direction. After giving the time delay to the output of the moving microphone according to the distance between two microphones,the cross spectrum and the coherence function are computed from the outputs of these microphones. The longer the span of the moving microphone, the sharper the directivity is. The beam is arbitrarily directed by adjusting phase in the case of three or more moving microphones.