Modeling Of Acoustic Environment Research Articles

Regression-Based Noise Modeling for Speech Signal Processing

Speech processing systems are very important in different applications involving speech and voice quality such as automatic speech recognition, forensic phonetics and speech enhancement, among others. In most of them, the acoustic environmental noise is added to the original signal, decreasing the signal-to-noise ratio (SNR) and the speech quality by consequence. Therefore, estimating noise is one of the most important steps in speech processing whether to reduce it before processing or to design robust algorithms. In this paper, a new approach to estimate noise from speech signals is presented and its effectiveness is tested in the speech enhancement context. For this purpose, partial least squares (PLS) regression is used to model the acoustic environment (AE) and a Wiener filter based on a priori SNR estimation is implemented to evaluate the proposed approach. Six noise types are used to create seven acoustically modeled noises. The basic idea is to consider the AE model to identify the noise type and estimate its power to be used in a speech processing system. Speech signals processed using the proposed method and classical noise estimators are evaluated through objective measures. Results show that the proposed method produces better speech quality than state-of-the-art noise estimators, enabling it to be used in real-time applications in the field of robotic, telecommunications and acoustic analysis.

EFFECT OF VARIATIONS IN MICROPHONE COVER SHAPE AND WEARING POSITION ON THE PERFORMANCE OF A HEARING-SUPPORT DEVICE MOUNTED ON EAR — SIMULATION STUDY

Morphological and positional factors that can affect the actual performance of the hearing-support (HS) devices are utilized to support the damaged hearing ability of the sensorineural hearing-impaired persons. However, there have been few studies that demonstrated the effects of variations in such design factors on the frequency response of the device experimentally. In this study, the effect of design variations in the shape of the microphone cover on the housing and the wearing position of the device mounted on the ear on the input frequency response of the device and on the performance of an embedded beamforming algorithm were investigated using a human upper body model, a hearing aid housing model, and an acoustic environment model using computer simulation. Experimental results showed that the implemented simulator could simulate the actual acoustic situations (differences less than 5 dB in audible frequency range) and that both of the response patterns of the device and beamforming algorithm were varied in accordance with the variations in the shape of the microphone cover and the mounting position of the device on the ear. These results demonstrate the necessity of additional design and algorithm fine-tuning of each (HS) device to improve its actual speech enhancement performance.

Comparative studies of the auditory periphery of deep‐sea fish.

A method and apparatus are provided for determining uncertainty in noise reduction based on a parametric model of speech distortion. The method is first used to reduce noise in a noisy signal. In particular, noise is reduced from a representation of a portion of a noisy signal to produce a representation of a cleaned signal by utilizing an acoustic environment model. The uncertainty associated with the noise reduction process is then computed. In one embodiment, the uncertainty of the noise reduction process is used, in conjunction with the noise-reduced signal, to decode a pattern state.

Enhancement of Log Mel Power Spectra of Speech Using a Phase-Sensitive Model of the Acoustic Environment and Sequential Estimation of the Corrupting Noise

This paper presents a novel speech feature enhancement technique based on a probabilistic, nonlinear acoustic environment model that effectively incorporates the phase relationship (hence phase sensitive) between the clean speech and the corrupting noise in the acoustic distortion process. The core of the enhancement algorithm is the MMSE (minimum mean square error) estimator for the log Mel power spectra of clean speech based on the phase-sensitive environment model, using highly efficient single-point, second-order Taylor series expansion to approximate the joint probability of clean and noisy speech modeled as a multivariate Gaussian. Since a noise estimate is required by the MMSE estimator, a high-quality, sequential noise estimation algorithm is also developed and presented. Both the noise estimation and speech feature enhancement algorithms are evaluated on the Aurora2 task of connected digit recognition. Noise-robust speech recognition results demonstrate that the new acoustic environment model which takes into account the relative phase in speech and noise mixing is superior to the earlier environment model which discards the phase under otherwise identical experimental conditions. The results also show that the sequential MAP (maximum a posteriori) learning for noise estimation is better than the sequential ML (maximum likelihood) learning, both evaluated under the identical phase-sensitive MMSE enhancement condition.

A rule-based interactive behavioral animation system for humanoids

We present a versatile, behavioral, and rule-based animation system that includes autonomous humanoid actors whose behavior is based on synthetic sensors that are used for perceiving the virtual environment. We combine the following in a consistent approach: L-systems, a behavioral production rule system; a particle system; an acoustic environment model, including a speech recognition module; a virtual life network; and a humanoid library. Together, these systems create a real-time-structured virtual environment that both high-level autonomous humanoids and interactive users can easily share.

Modeling of acoustic environment for the purpose of noise cancellation

The specific noise‐cancellation problem being considered here is the active cancellation of acoustic noise by the use of electronic earphones. Since it is important to achieve cancellation of ambient noise only, and not of warning sounds and speech, an adequate understanding of the noise environment in which the earphones are to be used is required. A key step in the approach taken by these authors is ton initially extract information regarding specific noise environments off‐line. This information can then be used to define details of control structure and values for the canceler in that environment. During actual usage of the earphones, the algorithm must adapt to fine‐tune itself to the environment at that time. Recordings of ambient noise were made for specific environments. Data‐analysis programs were developed and used to construct a model of the stationary noise environment and extract the relevant parameters. These are used to define the structure, parameter range, and computational requirements of the noise canceler. [Work supported by the Florida High Technology and Industry Council.]