Model Of Human Auditory System Research Articles

Toward Optimum Quantification of Pathology-Induced Noises: An Investigation of Information Missed by Human Auditory System

Clinical diagnosis of voice disorder and evaluation of therapy outcome heavily rely on accurate quantification of voice quality, which is closely tied to the physiology and function of the laryngeal mechanism. Considering the evaluation methodology of the voice, two main categories of auditory-perceptual assessment and acoustic analysis can be identified. This article presents a new approach for acoustic analysis of voice quality, which brings several advantages to the field. The proposed approach is non-parametric in the sense that it does not require the estimation of the fundamental frequency or spectral response of the vocal tract. This reduces the computational complexity of the measurement and reduces the possible errors due to inaccurate estimation of those parameters. Additionally, the method does not make any assumption about the phonetic context and hence has the potential to be applied to connected speech. The proposed method benefits from the multiresolution structure of the wavelet analysis for estimating the noisy component of a voice in the spectro-temporal domain. The informativeness of the estimated noise for voice quality distinction is examined based on different noise-quantification approaches. It is shown that deviation from the model of the human auditory system (HAS) leads to performance improvement. Through several analyses, it is argued that using models of HAS for quantification of the noise leads to significant loss of information relevant to voice quality. Findings from this article suggest that perception-based measures of voice quality are highly restricted in capturing important aspects of acoustic that could assist with voice quality distinctions. This characteristic is inherent to HAS and cannot be alleviated, highlighting a significant limitation of perception-based measures.

Audio steganalysis based on reversed psychoacoustic model of human hearing

During the last decade, audio information hiding has attracted lots of attention due to its ability to provide a covert communication channel. On the other hand, various audio steganalysis schemes have been developed to detect the presence of any secret messages. Basically, audio steganography methods attempt to hide their messages in areas of time or frequency domains where human auditory system (HAS) does not perceive. Considering this fact, we propose a reliable audio steganalysis system based on the reversed Mel-frequency cepstral coefficients (R-MFCC) which aims to provide a model with maximum deviation from HAS model. Genetic algorithm is deployed to optimize dimension of the R-MFCC-based features. This will both speed up feature extraction and reduce the complexity of classification. The final decision is made by a trained support vector machine (SVM) to detect suspicious audio files. The proposed method achieves detection rates of 97.8% and 94.4% in the targeted (Steghide@1.563%) and universal scenarios. These results are respectively 17.3% and 20.8% higher than previous D2-MFCC based method.

Speech enhancement based on undecimated wavelet packet-perceptual filterbanks and MMSE–STSA estimation in various noise environments

In this paper, we proposed a new speech enhancement system, which integrates a perceptual filterbank and minimum mean square error–short time spectral amplitude (MMSE–STSA) estimation, modified according to speech presence uncertainty. The perceptual filterbank was designed by adjusting undecimated wavelet packet decomposition (UWPD) tree, according to critical bands of psycho-acoustic model of human auditory system. The MMSE–STSA estimation (modified according to speech presence uncertainty) was used for estimation of speech in undecimated wavelet packet domain. The perceptual filterbank provides a good auditory representation (sufficient frequency resolution), good perceptual quality of speech and low computational load. The MMSE–STSA estimator is based on a priori SNR estimation. A priori SNR estimation, which is a key parameter in MMSE–STSA estimator, was performed by using “decision directed method.” The “decision directed method” provides a trade off between noise reduction and signal distortion when correctly tuned. The experiments were conducted for various noise types. The results of proposed method were compared with those of other popular methods, Wiener estimation and MMSE–log spectral amplitude (MMSE–LSA) estimation in frequency domain. To test the performance of the proposed speech enhancement system, three objective quality measurement tests (SNR, segSNR and Itakura–Saito distance (ISd)) were conducted for various noise types and SNRs. Experimental results and objective quality measurement test results proved the performance of proposed speech enhancement system. The proposed speech enhancement system provided sufficient noise reduction and good intelligibility and perceptual quality, without causing considerable signal distortion and musical background noise.

A medical hardware complex for speech signal research in patients with phonation disorders

The main elements of a medical hardware complex for speech signal research in patients with phonation disorders are considered. The complex is based on a model of human auditory system. It is shown that the complex can be used for speech recovery and diagnosis of the state of speech production apparatus.

Sound source tracking for security camera using sensor array utilizing object-related transfer function

A frequency-domain binaural model has been proposed as a computer model of human auditory system, and it can estimate the direction of arrival (DOA) of target sound source with only two inputs [Nakashima et al., Acoust. Sci & Tech. 21, 172–178 (2003)]. It utilizes a database of interaural level and phase differences obtained from head-related transfer function (HRTF) for DOA estimation. Since the interaural level and phase differences arise from the diffraction of head, similar ones can be obtained from the diffraction of an object, such as a transparent cover of security camera. In this paper, a source tracking method which utilizes an object-related transfer function is proposed. Three sensors attached on a dome-shaped camera cover are used, and the interchannel level and phase differences obtained from object-related transfer function are used for DOA estimation. The performance of sound source tracking is discussed based on the accuracy of DOA estimation as well as observation latency by the computer simulation. [Research partially supported by the MEXT, Grant-in-Aid for Scientific Research (C), 2006, 18500135 and carried out by the Cooperative Research Project Program of the RIEC, Tohoku University (Grant ♯H16.A02).]

Computer software for identification of noise source and automatic noise measurement

A new computational system for the environmental noise measurement and analysis has been developed. The system consists of binaural microphones, a laptop PC, and analysing software. A target noise is recorded automatically depending on the specified background noise level, and the acoustical parameters are calculated simultaneously. These functions allow for precise field measurements. The system is equipped with a template-matching algorithm for the identification of noise source. This function was implemented to avoid the effect of an interrupting sound such as voice and wind blowing during a measurement. Noise analyses in this system are based on the model of human auditory system. In addition to the time-series data of sound level, the important acoustical parameters of noise source are extracted from the running autocorrelation function (ACF) and the inter-aural cross-correlation function (IACF). It has been found that those parameters are strongly related to the auditory primary sensations and spatial sensations. Evaluation of the environmental noise based on these functions is another feature of this system. This paper describes the effectiveness of the ACF and the IACF analysis for analysing acoustical properties of noise and for evaluating the subjective response to noise.

A combined wideband speech and audio coder using human articulatory and auditory models

The modern high-quality speech and audio coding algorithms are mostly based on human articulatory models. On the other hand, high-quality audio coders are mostly based on models of human auditory system. However, neither of these two methods can provide acceptable performance for both music and speech. In this paper a coder is proposed which operates for both wideband speech and audio signal. The structure of the coder is based on both human articulatory and auditory models. In the first part the LPC parameters are drawn using the conventional autocorrelation method in order to simulate the human articulatory system. With LPC parameters the redundancy of input signal in time domain is removed. In the second part of the coder, the LPC residual signal is further transformed into frequency domain through MLT (modulated lapped transform). The MLT coefficients are then quantized in frequency domain using the human auditory properties such as masking property. The informal listening test has shown that the proposed coder provides very good quality for wideband speech and for most music signals.

Automatic detection of speech properties by the use of neural networks and human auditory system model: [formula omitted]. CRIM-McGill University, School of Computer Science 805, Sherbrooke str. W., Montreal, Quebec, H3A 2K6 Canada

Automatic detection of speech properties by the use of neural networks and human auditory system model: [formula omitted]. CRIM-McGill University, School of Computer Science 805, Sherbrooke str. W., Montreal, Quebec, H3A 2K6 Canada