Acoustic echo and noise canceller for personal hands-free video IP phone

This paper presents implementation and evaluation of a proposed acoustic echo and noise canceller (AENC) for videotelephony-enabled personal hands-free Internet protocol (IP) phones. This canceller has the following features: noise-robust performance, low processing delay, and low computational complexity. The AENC employs an adaptive digital filter (ADF) and noise reduction (NR) methods that can effectively eliminate undesired acoustic echo and background noise included in a microphone signal even in a noisy environment. The ADF method uses the step-size control approach according to the level of disturbance such as background noise; it can minimize the effect of disturbance in a noisy environment. The NR method estimates the noise level under an assumption that the noise amplitude spectrum is constant in a short period, which cannot be applied to the amplitude spectrum of speech. In addition, this paper presents the method for decreasing the computational complexity of the ADF process without increasing the processing delay to make the processing suitable for real-time implementation. The experimental results demonstrate that the proposed AENC suppresses echo and noise sufficiently in a noisy environment; thus, resulting in natural-sounding speech

DNN-Based Source Enhancement to Increase Objective Sound Quality Assessment Score

We propose a training method for deep neural network (DNN)-based source enhancement to increase objective sound quality assessment (OSQA) scores such as the perceptual evaluation of speech quality (PESQ). In many conventional studies, DNNs have been used as a mapping function to estimate time-frequency masks and trained to minimize an analytically tractable objective function such as the mean squared error (MSE). Since OSQA scores have been used widely for soundquality evaluation, constructing DNNs to increase OSQA scores would be better than using the minimum-MSE to create highquality output signals. However, since most OSQA scores are not analytically tractable, i.e., they are black boxes, the gradient of the objective function cannot be calculated by simply applying back-propagation. To calculate the gradient of the OSQA-based objective function, we formulated a DNN optimization scheme on the basis of black-box optimization, which is used for training a computer that plays a game. For a black-box-optimization scheme, we adopt the policy gradient method for calculating the gradient on the basis of a sampling algorithm. To simulate output signals using the sampling algorithm, DNNs are used to estimate the probability-density function of the output signals that maximize OSQA scores. The OSQA scores are calculated from the simulated output signals, and the DNNs are trained to increase the probability of generating the simulated output signals that achieve high OSQA scores. Through several experiments, we found that OSQA scores significantly increased by applying the proposed method, even though the MSE was not minimized

Time-domain estimation of acoustic radiation modes and active structural acoustic control

This paper presents a method of calculating the radiated sound power of vibrating structures based on the time domain estimation of acoustic radiation modes (ARMs). Each ARM is frequency-dependent, radiates power independent of the other ARMs and can be estimated in the time domain from measurements made at discrete sensor locations on the surface of the radiating structure. The individual ARM components are estimated digitally in the time domain using finite impulse response filters, which are designed to provide a best weighted fit to the ARMs in the frequency domain. The ARM amplitudes are estimated by filtering the vectors of measured velocities at points on the radiating surface with these ARM filters, before summing the product of the square of these amplitudes with the relevant eigenvalues to estimate the radiated sound power. The method is described with reference to a simply supported beam model. The results show that the sound power calculated from the proposed approach and from a frequency domain approach are comparable. Finally, a time domain feedforward active structural acoustic control system developed using the proposed method is presented and time domain simulations demonstrate the performance of the system

Adaptive control of radiated sound power based on time-domain estimates of acoustic radiation modes

In active structural acoustic control, broadband control of the radiated sound power from a structure can be achieved by minimizing the amplitudes of the acoustic radiation modes (ARMs). The shape of these ARMs is frequency dependent and only a few might radiate significant power in a given frequency range. In this paper a method is described by which the ARMs are estimated in real-time from a number of point response measurements taken on a vibrating structure. These estimates can be used to calculate the radiated power or, here, in a feedforward adaptive control system. Estimates of the ARM amplitudes in the time domain are produced by digitally filtering the outputs of an array of sensors mounted on the radiator. These filters are designed by FIR filters in the frequency domain based on the frequency-dependent ARMs and implemented in the time domain. These estimates are then used as the cost function in a feedforward, adaptive, filtered-X LMS controller. The theory is described with reference to a 2-dimensional vibrating structure. Finally numerical results of real-time simulations are presented