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.