음성 및 잡음 인식 알고리즘을 이용한 환경 배경잡음의 제거

The basic problem suffered by a communication system is in the removal of additive background noise.Spectral Subtraction method (SSM) has been successfully implemented to suppress the background noise.This paper proposed a novel hybrid system for enhancing noise-corrupted speech which involves a parallel combination of Spectral Subtraction and soft thresholding method in wavelet domain for speech enhancement.The performance of hybrid system is compared with both Spectral Subtraction method and Wavelet thresholding technique (WTT) and found to have better SNR improvement.Implementation and evaluation have been made using MATLAB.

This paper proposes a speaker-dependent speech recognition algorithm which can classify the gender for male and female speakers in white noise and car noise, using a neural network. The proposed speech recognition algorithm is trained by the neural network to recognize the gender for male and female speakers, using LPC (Linear Predictive Coding) cepstrum coefficients. In the experiment results, the maximal improvement of total speech recognition rate is 96% for white noise and 88% for car noise, respectively, after trained a total of six neural networks. Finally, the proposed speech recognition algorithm is compared with the results of a conventional speech recognition algorithm in the background noisy environment.

The spectral subtraction method is a conventional approach for single channel speech enhancement. The basic principle of this method is to estimate the short-time spectral magnitude of speech by subtracting estimated noise from the noisy speech spectrum and to combines it with the phase of the noisy speech. Besides reducing the noise, this method generates an unnatural and unpleasant noise, called remnant noise. This paper proposes a novel algorithm to reduce the remnant noise, and thus improving the overall quality of the enhanced speech. In this algorithm, the output of multi-band spectral subtraction (MBSS) method is used as the input signal again for next iteration process. After the MBSS method, the additive noise is changed to remnant noise. The remnant noise is re-estimated at each iteration. The new estimated noise, furthermore, is been used to process the next MBSS. This procedure is iterated a small number of times. The simulation results as well as informal subjective evaluations prove that the speech enhanced by proposed algorithm is more pleasant to listeners than the conventional MBSS algorithm. This reveals that the proposed algorithm reduces remnant noise satisfactorily and produces good speech quality with improved signal-to-noise ratio.

A time-frequency spectral subtraction method based on the knowledge of several psychoacoustic properties of human perception is presented. These effects are the critical band filtering, synaptic adaptation which also introduces temporal forward masking, equal loudness preemphasis, power law of hearing, and simultaneous masking effect. The perceptual speech and noise is estimated separately by a detailed psychoacoustic non-linear transformation undergoing in the human auditory system. The spectral subtraction using a over-subtraction factor and a spectral floor is measured by a speech recognition front-end using a continuous density HMM recognizer. The method shows reduced residual noise and improved word recognition performance in broadband Gaussian noise conditions compared to conventional spectral subtraction method.

Artificial neural networks (ANN) have been widely used in the field of data classification. Normally, training of neural network is applied with the traditional back propagation technique. As, this approach has various drawbacks, training of neural network is done with Particle Swarm Optimization (PSO). PSO has been widely used to solve the diverse kind of optimization problems. Population initialization performs a significant role in meta-heuristic algorithms. This paper describes a new initialization population approach Log Logistic termed as PSOLL-NN to create the initialization of the swarm. The proposed algorithm has been tested for weight optimization of feed forward neural network; and compared with back propagation Algorithm (BPA), standard PSO (PSO-NN), PSO initialized with Halton Sequence (PSOH-NN), Torus sequence (PSOT-NN) and Sobol sequence (PSOS-NN). The experimental results show that the proposed technique performed exceptionally better than the other traditional techniques. Moreover, the outcome of our work presents a foresight that how the proposed initialization technique can be used as an efficient alternative to standard training approaches for the data classification problems.

Rindik is a traditional music instrument originated from Bali that consists of 11 bamboo rods and played by a person hitting the bamboo rod with a rubber mallet in each player’s hands. Documentation of Rindik songs with automatic music transcription work is easier to do by separating it first. To overcome that challenge, two Rindik rods sound were separated using the spectral subtraction method. The noise spectrum is the spectrum of a single rod sound that needs to be muffled. The resulting audio is the other single rod sound and vice versa to get both single rod sound. The data consisted of single rod hit sound recordings of 11 single rod sound and 55 combinations of two-rods sounds being hit at the same time. The performance of the spectral subtraction method in separating Rindik sound was measured with MSE and SIR and also heard the noise exists in the separated audio signal. The experiment demonstrated a state-of-the-art performance consists of spectral subtraction with squared noise average magnitude with average MSE value 0.0126 and average SIR value 55.68 dB.

Supplementary schemes to spectral subtraction for speech enhancement

There are a number of problems associated with training neural networks with backpropagation algorithm. The algorithm scales exponentially with increased complexity of the problem. It is very often trapped in local minima, and is not robust to changes of network parameters such as number of hidden layer neurons and learning rate. The use of genetic algorithms is a recent trend, which is good at exploring a large and complex search space, to overcome such problems. In this paper a genetic algorithm is proposed for training feedforward neural networks and its performances is investigated. The results are analyzed and compared with those obtained by the backpropagation algorithm.

Vibration-based Structural Health Monitoring (SHM) systems use field measurements of operational signals, which are distorted by noise from many sources. Reducing this noise allows a more accurate assessment of the original "clean" signal and improves analysis results. The implementation of a noise reduction methodology for the Modal Distribution Method (MDM) is reported here. The spectral subtraction method is a popular broadband noise reduction technique used in speech signal processing. Its basic principle is to subtract the magnitude of the noise from the total noisy signal in the frequency domain. The underlying assumption of the method is that noise is additive and uncorrelated with the signal. In speech signal processing, noise can be measured when there is no signal. In the MDM, however, the magnitude of the noise profile can be estimated only from the magnitude of the Power Spectral Density (PSD) at higher frequencies than the frequency range of the true signal associated with structural vibrations under the additional assumption of white noise. The implementation of the spectral subtraction method to MDM may decrease the energy of the individual mode. In this work, a modification of the spectral subtraction method is introduced that enables the conservation of the energies of individual modes. The main difference is that any (negative) bars with a height below zero after subtraction are set to the absolute value of their height. Both noise reduction methods are implemented in the MDM, and an application example is presented that demonstrates its effectiveness when used with a signal corrupted by noise.

Speech enhancement using a Bayesian evidence approach

An Iterative Graph Spectral Subtraction Method for Speech Enhancement

In this paper, we provide a new theoretical analysis of the amount of musical noise generated via generalized spectral subtraction based on higher order statistics. Power spectral subtraction is the most commonly used spectral subtraction method, and in our previous study a musical noise assessment theory limited to the power spectral domain was proposed. In this paper, we propose a generalization of our previous theory on spectral subtraction for arbitrary exponent parameters. We can thus compare the amount of musical noise between any exponent domains from the results of our analysis. We also clarify that less musical noise is generated when we choose a lower exponent spectral domain; this implies that there is no theoretical justification for using power/amplitude spectral subtraction.

Speech Recognition Based on Efficient DTW Algorithm and Its DSP Implementation

In applying neural network to identification, back propagation (BP) algorithm is usually trapped to a local optimum and has a low speed of convergence, whereas particle swarm optimization (PSO) is advantageous in terms of global optimal searching. In this paper, a new algorithms which combines guaranteed convergence particle swarm optimizer (GCPSO) algorithm with BP (Back Propagation) algorithm is introduced in this paper and applied to BP neural networks to optimize the parameters of BP neural networks so as to improve the convergence speed and precision of BP neural networks. Compared with the BP algorithm and PSO-BP algorithm, the results of the simulation show that the algorithm of BP Neural Network Model Based on Guaranteed Convergence Particle Swarm Optimization speeds up the convergence process and enhances the accurate rate in pattern recognition.

This paper presents some results of a 64 channel digital hearing aid firmware development. Along with nonlinear voice compression, we present noise reduction, microphone & receiver calibration and in-situ algorithms with some results. The FFT-iFFT based compression method can generalize the nonlinear arbitrary compression scheme in connection with spectral noise reduction scheme. Spectral subtraction method was used for noise reduction, but noise spectrum was continuously updated by detecting signal level.