DSP hardware software co-design of audio de-noising algorithm
20 Nov 2014-pp 672-677
TL;DR: This paper focuses on developing a hardware software co-design of an audio de-noising algorithm based on spectral subtraction technique and an FPGA implementation of the proposed hardware is achieved utilizing Xilinx Spartan 6 device.
Abstract: As technology advances, more complicated systems requiring sophisticated algorithms often involving very complex digital signal processing (DSP) techniques are required. Therefore, hardware implementation of DSP algorithms has gained much attention during past years. One of the major applications of DSP is speech enhancement by eliminating the background noise. Spectral subtraction techniques in audio de-noising are widely used in real time applications. This paper focuses on developing a hardware software co-design of an audio de-noising algorithm based on spectral subtraction technique. An FPGA (Field Programmable Gate Array) implementation of the proposed hardware is achieved utilizing Xilinx Spartan 6 device and its performance estimation is also carried out in terms of resource utilization and timing requirement.
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TL;DR: A stand-alone noise suppression algorithm that resynthesizes a speech waveform and can be used as a pre-processor to narrow-band voice communications systems, speech recognition systems, or speaker authentication systems.
Abstract: A stand-alone noise suppression algorithm is presented for reducing the spectral effects of acoustically added noise in speech. Effective performance of digital speech processors operating in practical environments may require suppression of noise from the digital wave-form. Spectral subtraction offers a computationally efficient, processor-independent approach to effective digital speech analysis. The method, requiring about the same computation as high-speed convolution, suppresses stationary noise from speech by subtracting the spectral noise bias calculated during nonspeech activity. Secondary procedures are then applied to attenuate the residual noise left after subtraction. Since the algorithm resynthesizes a speech waveform, it can be used as a pre-processor to narrow-band voice communications systems, speech recognition systems, or speaker authentication systems.
4,862 citations
02 Apr 1979
TL;DR: This paper describes a method for enhancing speech corrupted by broadband noise based on the spectral noise subtraction method, which can automatically adapt to a wide range of signal-to-noise ratios, as long as a reasonable estimate of the noise spectrum can be obtained.
Abstract: This paper describes a method for enhancing speech corrupted by broadband noise. The method is based on the spectral noise subtraction method. The original method entails subtracting an estimate of the noise power spectrum from the speech power spectrum, setting negative differences to zero, recombining the new power spectrum with the original phase, and then reconstructing the time waveform. While this method reduces the broadband noise, it also usually introduces an annoying "musical noise". We have devised a method that eliminates this "musical noise" while further reducing the background noise. The method consists in subtracting an overestimate of the noise power spectrum, and preventing the resultant spectral components from going below a preset minimum level (spectral floor). The method can automatically adapt to a wide range of signal-to-noise ratios, as long as a reasonable estimate of the noise spectrum can be obtained. Extensive listening tests were performed to determine the quality and intelligibility of speech enhanced by our method. Listeners unanimously preferred the quality of the processed speech. Also, for an input signal-to-noise ratio of 5 dB, there was no loss of intelligibility associated with the enhancement technique.
1,352 citations
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TL;DR: In this proposed method adaptive wavelet thresholding and modified thresholding functions are introduced to improve the speech enhancement performance as well as the automatic speech recognition (ASR) accuracy.
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