In this letter, we propose an acoustic distance rendering (ADR) algorithm that can efficiently create the proximity effect in virtual reality (VR) systems. By observing the variation of acoustic cues caused by the movement of the sound source in the near field, we develop a model that can closely approximates the near-field transfer function (NFTF). The developed model is used to efficiently compensate for the near-field effect on the head related transfer function (HRTF). The proposed algorithm is implemented and tested in the form of an audio plugin for a VR platform and the test results confirm the efficiency of the proposed algorithm.

Sound source localization is an essential technique in many applications, e.g., speech enhancement, speech capturing and human-robot interaction. However, the performance of traditional methods degrades in noisy or reverberant environments, and it is sensitive to the spatial location of sound source. To solve these problems, we propose a sound source localization framework based on bi-direction interaural matching filter (IMF) and decision weighting fusion. Firstly, bi-directional IMF is put forward to describe the difference between binaural signals in forward and backward directions, respectively. Then, a hybrid interaural matching filter (HIMF), which is obtained by the bi-direction IMF through decision weighting fusion, is used to alleviate the affection of sound locations on sound source localization. Finally, the cosine similarity between the HIMFs computed from the binaural audio and transfer functions is employed to measure the probability of the source location. Constructing the similarity for all the spatial directions as a matrix, we can determine the source location by Maximum A Posteriori (MAP) estimation. Compared with several state-of-the-art methods, experimental results indicate that HIMF is more robust in noisy environments.

Sound source localization (SSL), with a binaural input in practical environments, is a challenging task due to the effects of noise and reverberation. In psychoacoustic research field, one of the theories to explain the mechanism of human perception in such environments is the well-known equalization-cancellation (EC) model. Motivated by the EC theory, this paper investigates a binaural SSL method by integrating EC procedures into a beamforming technique. The principle idea is that the EC procedures are first utilized to eliminate the sound signal component at each candidate direction respectively; direction of sound source is then determined as the direction at which the residual energy is minimal. The EC procedures applied in the proposed method differ from those in traditional EC models, in which the interference signals in rooms are accounted in E and C operations based on limited prior known information. Experimental results demonstrate that our proposed method outperforms the traditional SSL algorithms in the presence of noise and reverberation simultaneously.

We compared two audio output devices for augmented audio reality applications. In these applications, we plan to use speech annotations on top of the actual ambient environment. Thus, it becomes essential that these audio output devices are able to deliver intelligible speech annotation along with transparent delivery of the environmental auditory scene. Two candidate devices were compared. The first output was the bone-conduction headphone, which can deliver speech signals by vibrating the skull, while normal hearing is left intact for surrounding noise since these headphones leave the ear canals open. The other is the binaural microphone/earphone combo, which is in a form factor similar to a regular earphone, but integrates a small microphone at the ear canal entry. The input from these microphones can be fed back to the earphones along with the annotation speech. We also compared these devices to normal hearing (i.e., without headphones or earphones) for reference. We compared the speech intelligibility when competing babble noise is simultaneously given from the surrounding environment. It was found that the binaural combo can generally deliver speech signals at comparable or higher intelligibility than the bone-conduction headphones. However, with the binaural combo, we found that the ear canal transfer characteristics were altered significantly by shutting the ear canals closed with the earphones. Accordingly, if we employed a compensation filter to account for this transfer function deviation, the resultant speech intelligibility was found to be significantly higher. However, both of these devices were found to be acceptable as audio output devices for augmented audio reality applications since both are able to deliver speech signals at high intelligibility even when a significant amount of competing noise is present. In fact, both of these speech output methods were able to deliver speech signals at higher intelligibility than natural speech, especially when the SNR was low.

This paper presents a Complex-Valued Neural Network-based sound localization method. The proposed approach uses two microphones to localize sound sources in the whole horizontal plane. The method uses time delay and amplitude difference to generate a set of features which are then classified by a Complex-Valued Multi-Layer Perceptron. The advantage of using complex values is that the amplitude information can naturally masks the phase information. The proposed method is analyzed experimentally with regard to the spectral characteristics of the target sounds and its tolerance to noise. The obtained results emphasize and confirm the advantages of using Complex-Valued Neural Networks for the sound localization problem in comparison to the traditional Real-Valued Neural Network model.

The frequency domain binaural model (FDBM) has been previously proposed to localize multiple sound sources. Since the method requires only two input signals and uses interaural phase and level differences caused by the diffraction generated by the head, flexibility in application is very high when the head is considered as an object. When an object is symmetric with respect to the two microphones, the performance of sound source localization is degraded, as a human being has front-back confusion due to the symmetry in a median plane. This paper proposes to reduce the degradation of performance on sound source localization by a combination of the microphone pair outputs using the FDBM. The proposed method is evaluated by applying to a security camera system, and the results showed performance improvement in sound source localization because of reducing the number of cones of confusion.

A binaural hearing assistance system based on the frequency domain binaural model has been previously proposed. The system can enhance a signal coming from a specific direction. Since the system utilizes a binaural signal, an inter-channel communication between left and right subsystems is required. The bit rate reduction in inter-channel communication is essential for the detachment of the headset from the processing system. In this paper, the performance of a system which uses a differential pulse code modulation codec is examined and the relationship between the bit rate and sound quality is discussed.

3-D sound using head-related transfer functions (HRTFs) is applicable to embedded systems such as portable devices, since it can create spatial sound effect without multichannel transducers. Low-order modeling of HRTF with an IIR filter is effective for the reduction of the computational load required in embedded applications. Although modeling of HRTFs with IIR filters has been studied earnestly, little attention has been paid to sound movement with IIR filters, which is important for practical applications of 3-D sound. In this paper, a practical method for sound movement is proposed, which utilizes time-varying IIR filters and variable delay filters. The computational cost for sound movement is reduced by about 50% with the proposed method, compared to conventional low-order FIR implementation. In order to facilitate efficient implementation of 3-D sound movement, tradeoffs between the subjective quality of the output sound and implementation parameters such as the size of filter coefficient database and the update period of filter coefficients are also discussed.

In this paper, we address the blind separation problem of binaural mixed signals, and we propose a novel blind separation method, in which a self-generator for initial filters of Single-Input-Multiple-Output-model-based independent component analysis (SIMO-ICA) is implemented. The original SIMO-ICA which has been proposed by the authors can separate mixed signals, not into monaural source signals but into SIMO-model-based signals from independent sources as they are at the microphones. Although this attractive feature of SIMO-ICA is beneficial to the binaural sound separation, the current SIMO-ICA has a serious drawback in its high sensitivity to the initial settings of the separation filter. In the proposed method, the self-generator for the initial filter functions as the preprocessor of SIMO-ICA, and thus it can provide a valid initial filter for SIMO-ICA. The self-generator is still a blind process because it mainly consists of a frequency-domain ICA (FDICA) part and the direction of arrival estimation part which is driven by the separated outputs of the FDICA. To evaluate its effectiveness, binaural sound separation experiments are carried out under a reverberant condition. The experimental results reveal that the separation performance of the proposed method is superior to those of conventional methods.

HRTFs (head-related transfer functions) are available for sound field reproduction with spatial fidelity, since HRTFs involve the acoustic cues such as interaural time difference, interaural intensity difference and spectral cues that are used for the perception of the location of a sound image. Generally, FIR filters are used in the simulation of HRTFs. However, this method is not useful for a simply system, since the orders of the FIR filters are high. In this paper, we propose a method using IIR filter for simply realization of sound image localization. The HRTFs of a dummy-head were approximated by the following filters: (A) fourth to seventh-order IIR filters and (B) third-order IIR filters. In total, the HRTFs of 24 different directions on the horizontal plane were used as the target characteristics. Sound localization experiments for the direction and the elevation angle of a sound image were carried out for 3 subjects in a soundproof chamber. The binaural signal sounds using the HRTFs simulated by FIR filters and approximated by IIR filters (A) and (B) were reproduced via two loudspeakers, and sound image localization on the horizontal plane was realized. As the result of the experiments, the sound image localization using the HRTFs approximated by IIR filters (A) is the same accuracy as the case of using the FIR filters. This result shows that it is possible to create sound fields with binaural reproduction more simply.

A new method of designing a microphone array with two outputs preserving binaural information is proposed in this paper. This system employs adaptive beamforming using multiple constraints. The binaural cues may be preserved in the two outputs by use of these multiple constraints with simultaneous beamforming to enhance target signals is also available. A computer simulation was conducted to examine the performance of the beamforming. The results showed that the proposed array can perform both the generation of the binaural cues and the beamforming as intended. In particular, beamforming with double-constraints exhibits the best performance; DI is around 7 dB and good interchannel (interaural) time/phase and level differences are generated within a target region in front. With triple-constraints, however, the performance of the beamforming becomes poorer while the binaural information is better realized. Setting of the desired responses to give proper binaural information seems to become critical as the number of the constraints increases.

Electric fishes generate an AC electric field around themselves by the electric organ in the tail. Spatial distortion of the field by nearby objects is detected by an electroreceptor array located an over the body surface to localize the object electrically when other senses such as vision and mechanosense are useless. Each fish has its own 'frequency band' for its electric organ discharges, and jamming of the electrolocation system occurs when two fish with similar discharge frequencies encounter. To avoid janmming, the fish shift their discharge frequencies in appropriate directions. A computational algorithm for this electrical behavior and its neuronal implementation by the brain have been discovered. The design features of the system, however, are rather complex for this simple behavior and cannot be readily explained by functional optimization processes during evolution. To gain insights into the origin of the design features, two independently evolved electric fish species which perform the same behavior are compared. Complex features of the neuronal computation may be explained by the evolutionary history of neuronal elements.

The process of room acoustic planning & design can be aided by Binaural Technology. To this end, a three-stage modelling process is proposed that consists of a "sound"-specification phase, a design phase and a work-plan phase. Binaural recording, reproduction and room simulation techniques are used throughout the three phases allowing for subjective/objective specification and surveillance of the design goals. The binaural room simulation techniques involved include physical scale models and computer models of different complexity. Some basics of binaural computer modelling of room acoustics are described and an implementation example is given. Further the general structure of a software system that tries to model important features of the psychophysics of binaural interaction is reported. The modules of the model are: outer-ear simulation, middle-ear simulation, inner-ear simulation, binaural processors, and the final evaluation stage. Using this model various phenomena of sound localization and spatial hearing, such as lateralization, multiple-image phenomena, summing localization, the precedence effect, and auditory spaciousness, can be simulated. Finally, an interesting application of Binaural Technology is presented, namely, a so called Cocktail-Party-Processor. This processor uses the predescribed binaural model to estimate signal parameters of a desired signal which may be distored by any type of interfering signals. In using this strategy, the system is able to even separate the signals of competitive speakers.