The relationships between the amplitude and phase of the short-time Fourier transform (STFT) are investigated. By choosing the Gaussian window for the STFT, we reveal that the group delay and instantaneous frequency of each signal segment, both of which are derived from the phase by definition, can also be explicitly linked with the amplitude. As a result, the amplitude and phase can also be linked through the group delay or instantaneous frequency without making any assumptions for the phase property of the target signals, e.g., minimum, maximum, or linear phase. The theoretical basis is also confirmed in numerical simulations.

While heart disease is one of the three major diseases, only well-qualified doctors can evaluate phonocardiographic signals. This calls for an easily available system that can automatically diagnose phonocardiographic signals. When recording in a room, suppression is required as these signals are heavily contaminated by noise from various sources such as air conditioners and fans. Wavelet transform is one method for denoising phonocardiographic signals, but appropriate parameters are required. In this study, we investigated both normal and abnormal phonocardiographic signals to determine the appropriate use of single and multilevel thresholds and the best types of wavelet functions. The experiment results show that the most appropriate wavelet function is Symlet14 and multilevel thresholding is best for low SNRs.

Health promotion and maintenance is becoming increasingly important and depends on three elements: nutrition, exercise, and rest (sleep). In the present study, focusing on sleep, we develop a smartphone-based system based on snore activity detection to investigate day-to-day variations in the sleep state, which does not require dedicated hardware. Here, we analyze the number of training data required for snore activity detection using a support vector machine (SVM), and we consider ways to improve detection performance. The sound pressure level and mel-frequency cepstrum coefficients are calculated from sleep sound data obtained using a smartphone. Snore activity detection is performed by machine learning using an SVM with a linear kernel, the SVM is trained by labeled acoustic features, and the trained SVM models are used to detect snore activity. In general, the accuracy of the generated models increases with the increasing number of training data in the learning algorithm, which in turn increases the computational cost, therefore, a balance between accuracy and cost efficiency is much required. We investigate the relation between the detection rate and the number of training data in snore activity detection, and we propose the optimum number of data required for learning.

We propose a novel close-talking spherical microphone array that uses the residual signal between the observed sound pressure and the interpolated sound pressure at the center of the spherical array. The interpolated sound is obtained from the sound pressures observed on the surface of a sphere on the basis of the spherical harmonic expansion, assuming that the sound originates from the outside of the array. If the sound source is close to the spherical array, the array cannot express the spherical wave correctly because the number of microphones is limited. As a result, the residual signal increases. This method is a modified form of the conventional method, which interpolates the sound pressure by using the Kirchhoff integral equation. In contrast with the conventional method, we interpolate the sound at the center of the sphere by using only the average value of the sound pressures on the spherical array surface. The computer simulations were conducted using a 12-element spherical microphone array with radius of 5 cm. These results showed that the performances of both methods were almost equivalent, although the proposed method used half the number of microphones as the conventional method.

For sound field reproduction that includes height (with-height reproduction), it is more efficient to record and reproduce the sound field with lower resolution in elevation than in azimuth due to the spatial abilities of human auditory perception. We propose a sound field reproduction method using horizontally arranged cylindrical arrays of microphones and loudspeakers, which is based on the wave field reconstruction (WFR) filter. With the use of cylindrical array configurations, it is possible to reproduce sound waves arriving from upper and lower directions with a smaller number of array elements at angular positions. The WFR filter is analytically derived in the cylindrical harmonic domain and allows direct transformation from the received signals of the microphones into the driving signals of the loudspeakers. A model in which microphones are mounted on a rigid cylindrical baffle is introduced to stabilize the WFR filter. Numerical simulation results indicated that the reproduction accuracy in the neighboring region along the central axis of the cylindrical array was better preserved when using the proposed method than when the method with planar arrays was used.

For with-height reproduction of a sound field, an efficient technique is to record and reproduce the sound field at lower resolution at an elevation angle than that at a horizontal angle based on auditory perception. To achieve this, developing a method using multiple horizontal linear arrays of microphones and loudspeakers is necessary. We propose a sound field reproduction method for a cylindrical array configuration which is based on a wave field reconstruction (WFR) filter analytically derived in the helical wave spectrum domain. The filter is stabilized by introducing a model in which microphones are mounted on a rigid cylindrical baffle. Numerical simulation results indicated that the reproduction accuracy of the proposed method was well preserved in the near-field region along the central axis of the cylinder even when the number of elements at angular position is small.

In real-time sound field transmission systems, the driving signals of a loudspeaker array should be obtained using only the received signals of a microphone array. For efficient transformation of signals of planar or linear arrays, we previously proposed a method of applying a transform filter in the spatio-temporal frequency domain; this filter is defined as the wave field reconstruction (WFR) filter. In linear array configurations, the major artifact of this method is an increase in reverberation time and a decrease in the direct-to-reverberant energy ratio (DRR) because reflections from above and below the microphone array can not be distinguished. We propose a method combining circular harmonics beamforming with the WFR filter as a preprocess in order to match the reproduced DRR to the original one at the time the direct sound wave is properly reproduced. Simulation results indicated that the DRR reproduced using the proposed method was much closer to that reproduced by the method without beamforming.

A method for separating underdetermined sound sources based on a novel power spectral density (PSD) estimation is proposed. The method enables up toM(M-1)+1 sources to be separated when we use a microphone array ofMsensors and a Wiener post-filter calculated by the estimated PSDs. The PSD of a beamformer's output is modelled by a mixture of source PSDs multiplied by the beamformer's directivity gain in the particular angle where each source is located. Based on this model, the PSD of each sound source is estimated from the PSD of multiple fixed beamformers' outputs using the difference in the combination of directivity gains. Simulation results proved that the proposed method effectively separated up toM(M-1)+1 sound sources if the fixed beamformers were appropriately selected. Experiments were also conducted in a reverberant chamber to ensure the proposed method was also effective in practical use.

For transmission of a physical sound field in a large area, it is necessary to transform received signals of a microphone array into driving signals of a loudspeaker array to reproduce the sound field. We propose a method for transforming these signals by using planar or linear arrays of microphones and loudspeakers. A continuous transform equation is analytically derived based on the physical equation of wave propagation in the spatio-temporal frequency domain. By introducing spatial sampling, the uniquely determined transform filter, called a wave field reconstruction filter (WFR filter), is derived. Numerical simulations show that the WFR filter can achieve the same performance as that obtained using the conventional least squares (LS) method. However, since the proposed WFR filter is represented as a spatial convolution, it has many advantages in filter design, filter size, computational cost, and filter stability over the transform filter designed by the LS method.

We generalized our previously proposed diffused sensing for a microphone array design to achieve sharp directive beamforming to enable various filter design methods to be applied. In the conventional microphone array, various filter design methods have been studied to narrow the directivity beam width. However, it is difficult to minimize the power of interference sources in the beamforming output (output interference power) over a broad frequency range since the cross-correlation between transfer functions from sound sources to microphones increases in some frequencies. With the diffused sensing, the cross-correlation is minimized by physically varying the transfer functions. We investigated how a microphone array should be designed in order to minimize the cross-correlation between transfer functions and found that placing the array in a diffuse acoustic field produces optimum results. Because the transfer functions are known a priori, this finding makes it possible to narrow the directivity beam width over a broad frequency range. This observation can be practically achieved by placing microphones inside a reflective enclosure, part of which is open to let sound waves enter. We conducted experiments using 24 microphones and confirmed that the output interference power was reduced over a broad frequency range and the beam width was narrowed by using the diffused sensing.

We present a localized sound reproduction method in which sound energy is high in specified target areas and low everywhere else. Such a system needs to reproduce a sound which attenuates faster than a point source. Our study focuses on an acoustic evanescent wave that has faster distance attenuation. The evanescent wave can be generated when a wave transmitted at subsonic speed between two media. Previously, we used a linear loudspeaker array to reproduce the evanescent wave. Nevertheless, since this method requires a line source of infinite length, a large number of loudspeakers are needed to realize sufficiently fast distance attenuation. To reduce the number of loudspeakers, we propose an evanescent wave reproduction method by using a circular loudspeaker array. Computer simulation results show that our method achieves faster distance attenuation than a point source and reduces the number of loudspeakers to less than that of the linear loudspeaker array.

For real-time sound field transmission systems from a far-end to a near-end, the driving signals of a loudspeaker array at the near-end need to be calculated by using only received signals obtained by a microphone array at the far-end. Additionally, having the capability to control the location of the sound field to be reproduced in order to adjust it to the visual images is advantageous. The goal of this study was to develop a method to transform received signals of a microphone array into driving signals of a loudspeaker array in order to reproduce arbitrary shifted sound fields. We analytically derive a transform filter in the spatio-temporal frequency domain. The location of the sound field to be reproduced is controllable only by phase-shift of the transform filter. The proposed method was found to be computationally efficient compared to the conventional method based on a least squares algorithm, and numerical simulation results indicated that reproduction accuracies were almost the same in both methods.

A spatial sensitivity control method for segregating the sound sources in the same direction by using an acoustic reflector is proposed. Our goal is to clearly pick up the target source at an arbitrary position using a microphone array. Though many methods have been studied for spatial sensitivity control, it is difficult to robustly suppress the power of noise sources in the same direction of the target source in a room. To overcome this problem, we attach a reflector to a microphone array to capture the reflected sounds whose characteristics vary depending on the distance from the array to the source. Assuming that the acoustical properties of the reflector are known e.g., measuring the transfer functions, those reflected sounds can be used as effective clues for segregating the sound sources in the same direction. With the proposed method, a filter for minimizing the output noise power is derived by taking into consideration of the acoustic properties of the reflector. Experiments were conducted in an actual room by using 96 microphones and a large reflector. We confirmed that the spatial sensitivities for segregating the target source at an arbitrary position from noise sources can be achieved by using the proposed method.

We propose a method for achieving sharp directivity by sensing signals in a diffuse acoustic field. Directivity control based on a beamforming method has been studied to make it possible to extract the waveform and location of an identified target source even if there are many noise sources. Sharp directivity can be achieved by minimizing the output noise power of a beamforming filter. However, it is difficult to minimize the output noise power over a broad frequency ranges. Our approach for minimizing the output noise power is to control the spatial properties of the transfer functions and the spatial correlation matrix, by using a reflector that surrounds a microphone array. We investigated the relationships between the output noise power and the structure of the spatial correlation matrix and found that it was possible to minimize the output noise power by sensing diffuse acoustic signals and by designing filters taking the diffuseness of the acoustic field into consideration. In experiments, we observed diffusely reflected signals by placing a truncated-octahedral reflector near a spherical microphone array. We designed filters by using measured transfer functions and confirmed that the proposed method was effective for reducing the output noise power and forming a sharp directivity beamforming filter.

It has been possible to reproduce point sound sources between listeners and a loudspeaker array by using the focused-source method. However, this method requires physical parameters of the sound sources to be reproduced, such as source positions, directions, and original signals. This fact makes it difficult to apply the method to real-time reproduction systems because decomposing received signals into such parameters is not a trivial task. This paper proposes a method for recreating virtual sound sources in front of a planar or linear loudspeaker array. The method is based on wave field synthesis but extended to include the inverse wave propagator often used in acoustical holography. Virtual sound sources can be placed between listeners and a loudspeaker array even when the received signals of a microphone array equally aligned with the loudspeaker array are only known. Numerical simulation results are presented to compare the proposed and focused-source methods. A system was constructed consisting of linear microphone and loudspeaker arrays and measurement experiments were conducted in an anechoic room. When comparing the sound field reproduced using the proposed method with that using the focused-source method, it was found that the proposed method could reproduce the sound field at almost the same accuracy.

We investigated the multipole loudspeaker array based on spherical harmonic expansion in the Cartesian coordinate system. The arrangement of the multipoles with the least number of loudspeaker units has been studied for 2nd order of spherical harmonic expansion. The directivity of the multipole array does not achieve ideal directivity of the spherical functions due to the asymmetrical arrangement. The multipole array was also applied to an end-fire array because a multipole array uses less loudspeakers than the spherical array if the look-direction is fixed. We compared the performance of the conventional least-squares design method and multipole array method for an end-fire array. The least squares method had a higher directivity due to the large number of variable coefficients. Nevertheless, the multipole array based on spherical harmonic expansion has advantages of easy setup in the Cartesian coordinate system, analytical directivity, and small number of filter coefficients.

We present a method for estimating the direct-to-reverberant energy ratio (DRR) that uses a direct and reverberant sound spatial correlation matrix model (Hereafter referred to as the spatial correlation model). This model expresses the spatial correlation matrix of an array input signal as two spatial correlation matrices, one for direct sound and one for reverberation. The direct sound propagates from the direction of the sound source but the reverberation arrives from every direction uniformly. The DRR is calculated from the power spectra of the direct sound and reverberation that are estimated from the spatial correlation matrix of the measured signal using the spatial correlation model. The results of experiment and simulation confirm that the proposed method gives mostly correct DRR estimates unless the sound source is far from the microphone array, in which circumstance the direct sound picked up by the microphone array is very small. The method was also evaluated using various scales in simulated and actual acoustical environments, and its limitations revealed. We estimated the sound source distance using a small microphone array, which is an example of application of the proposed DRR estimation method.

A new approach for estimating the direct-to-reverberant energy ratio (DRR) using a microphone array is proposed. The method is based on a model of a spatial correlation matrix that segregates direct sound and reverberation. It estimates DRR from the power spectra of both components, which are derived from the correlation matrix of the observed signal. In experiments performed in simulated and actual reverberant environments, the proposed method mostly succeeded in estimating DRR accurately. We also present speech enhancement using binary masking as an example of an application of the estimated DRR. By utilization of the DRR as a factor to discriminate the distances of speakers, separation of speech signals whose sources were located in the same direction but at different distances was achieved.

We propose a method for estimating the sound source orientation by using the reflection sounds. The sound source orientation is important spatial information for promoting communication using teleconference systems. We assume that the observed signals captured using several microphones in a reverberant room are used for estimating the sound source orientation. Since the power of each reflection sound depends on the sound source orientation, the transfer functions between a sound source and multiple microphones are varied corresponding to the sound source orientation. We found that the eigenspace of spatial correlation constructed from the observed signals has a characteristic shape corresponding to the sound source orientation. We also proposed an efficient method for estimating the sound source orientation by matching the eigenspace of observed signals with pre-learned eigenspace models for every sound source orientation. In numerical experiments, we obtained about 80% accuracy. We confirmed the effectiveness of the proposed method.

A robust dereverberation method is presented for speech enhancement in a situation requiring adaptation where a speaker shifts his/her head under reverberant conditions causing the impulse responses to change frequently. We combine correlation-based blind deconvolution with modified spectral subtraction to improve the quality of inverse-filtered speech degraded by the estimation error of inverse filters obtained in practice. Our method computes inverse filters by using the correlation matrix between input signals that can be observed without measuring room impulse responses. Inverse filtering reduces early reflection, which has most of the power of the reverberation, and then, spectral subtraction suppresses the tail of the inverse-filtered reverberation. The performance of our method in adaptation is demonstrated by experiments using measured room impulse responses. The subjective results indicated that this method provides superior speech quality to each of the individual methods: blind deconvolution and spectral subtraction.

A dereverberation technique is developed to provide an alternative means of reducing reverberation in speech signals. The conventional MINT (the multiple-input/output inverse-filtering theorem) method uses the room impulse responses to calculate the inverse filters, so it cannot recover speech signals in practice, where the room impulse responses are unknown in advance. Our method blindly estimates the inverse filters by computing the correlation matrix between input signals that can be observed, instead of room impulse responses. We also combine the inverse filtering with modified spectral subtraction against the estimation error of inverse filters used in the field. The performance of the proposed method is demonstrated using actual room impulse responses

In hands-free speech recognition the speaker should be able to move freely in front of the speech acquisition device. However, the speech signal is then submitted to variations due to the continuous change of position in the acoustic space. This paper focuses on the role of speaker head rotations as compared with static situations in anechoic conditions. The effect of speaker directivity in speech recognition performance degradation is demonstrated and a compensation method based on HMM composition is proposed to increase the performance.

A new blind deconvolution method is proposed for recovering an unknown source signal, which is observed through two unknown channels characterized by non-minimum phase impulse response filters. Conventional methods cannot estimate the non-minimum phase parts. Our method is based on computing the eigenvector corresponding to the smallest eigenvalue of the input correlation matrix and using a cost function to determine the order of the impulse response filter model. Multi-channel inverse filtering with the estimated impulse responses is used to recover the unknown source signal. Sub-band processing is also used to reduce the complexity of dealing with long impulse responses such as room impulse responses. Computer simulation shows that the effectiveness of our method.