Digital processing of speech signals

A new way to enhance speech signal based on compressed sensing

Speech data collected under uncontrolled environment need to be processed to build a robust automatic speech recognition system. In this paper, a method is proposed to process the degraded speech signal. Initially, the significance of the spectral subtraction with voice activity detection (SS-VAD) and magnitude squared spectrum estimators are studied for different types of noises. In SS-VAD method, the degraded speech data is sampled and windowed into 50% overlapping. The VAD is used to detect the voiced regions of speech signal. The minimum mean square error-short time power spectrum, minimum mean square error-spectrum power based on zero crossing (MMSE-SPZC) and maximum a posteriori estimators are studied individually. These MSS estimators are implemented on the assumption that the magnitude squared spectrum of the degraded speech signal is the sum of the clean (original) speech signal and noise model. The experimental results show that the MMSE-SPZC estimator gives better performance compared to the other two methods. This estimator is combined with SS-VAD method to improve the performance. In this paper, the combined SS-VAD and MMSE-SPZC method, yields better speech quality by reducing noise in degraded speech signal compared to the individual methods.

Speech enhancement by combining spectral subtraction and minimum mean square error-spectrum power estimator based on zero crossing

We used Spectral Subtraction in this research as a method to remove noise from speech signals. Initially, the spectrum of the noisy speech is computed using the fast Fourier transform (FFT), then the average magnitude of the noise spectrum is subtracted from the noisy speech spectrum. We applied Spectral Subtraction to the speech signal "Hot dog" to which we digitally added vacuum cleaner noise. We implemented the noise removal algorithm by storing the noisy speech data into Hanning time-widowed half-overlapped data buffers, computing the corresponding spectrums using the FFT, removing the noise from the noisy speech, and reconstructing the speech back into the time domain using the inverse fast Fourier transform (IFFT). We had recourse to the speech to noise ratio (SNR )in order to evaluate the performance of the proposed algorithm.

Spectral Subtraction of Noise in Speech Processing Applications

A sound target-searching robot system which includes a 4-channel microphone array for sound collection, magneto-resistive sensor for declination measurement, and a wireless sensor networks (WSN) for exchanging information is described. It has an embedded sound signal enhancement, recognition and location method, and a sound searching strategy based on a digital signal processor (DSP). As the wireless network nodes, three robots comprise the WSN a personal computer (PC) in order to search the three different sound targets in task-oriented collaboration. The improved spectral subtraction method is used for noise reduction. As the feature of audio signal, Mel-frequency cepstral coefficient (MFCC) is extracted. Based on the K-nearest neighbor classification method, we match the trained feature template to recognize sound signal type. This paper utilizes the improved generalized cross correlation method to estimate time delay of arrival (TDOA), and then employs spherical-interpolation for sound location according to the TDOA and the geometrical position of the microphone array. A new mapping has been proposed to direct the motor to search sound targets flexibly. As the sink node, the PC receives and displays the result processed in the WSN, and it also has the ultimate power to make decision on the received results in order to improve their accuracy. The experiment results show that the designed three-robot system implements sound target searching function without collisions and performs well.

Design and Implementation of Sound Searching Robots in Wireless Sensor Networks.

In this research, we present a technique to increase noise removal from noisy speech signals using spectral subtraction. The noise removal algorithm includes storing the noisy speech data into Hanning time-widowed half-overlapped data buffers, computing the corresponding spectrums using the FFT, removing the noise from the noisy speech, and reconstructing the speech back into the time domain using the inverse fast Fourier transform (IFFT). Performance of the algorithm was evaluated by calculating the speech to noise ratio (SNR). The improvement technique involved varying the lengths of the Hanning time windows, as well as the degrees of data buffers overlapping. Further improvement was sought by using frames averaging technique, which consists in averaging various spectrum frames before removing the noise. Results showed that using one-fourth overlapped data buffers with 256 points Hanning windows and no frames averaging lead to the best performance in removing noise from the noisy speech.

C. Cole

Papers

Spectral Subtraction of Noise in Speech Processing Applications

Increasing Additive Noise Removal in Speech Processing Using Spectral Subtraction