Multi-band Spectral Subtraction Research Articles

An MDCT-domain audio denoising method with a block switching scheme

In this paper, an audio denoising method is proposed for improving the quality of handheld audio recording devices. The proposed method reduces noise differently depending on the block size in the modified discrete cosine transform (MDCT) analysis of an audio coder. Specifically, denoising for a long block is performed by multi-band spectral subtraction (MBSS) with perceptually weighted scalefactor bands, while that for a short block is performed by subband power scaling to maintain coherence of power with the previously-denoised long block. In order to evaluate the performance of the proposed method, it is first embedded into MPEG-2 advanced audio coding (AAC) that is popularly used for audio recording devices. Then, its performance is compared with that of a conventional audio denoising method based on block thresholding in terms of cepstral distortion, subjective quality, and computational complexity. It is shown from performance comparison that the proposed method outperforms the block thresholding method in both objective and subjective measurements. Moreover, the complexity of the proposed method is sufficiently lowered to be implemented on most resource-constrained handheld audio recording devices, unlike the conventional method.

Mechanical noise suppression based on non-negative matrix factorization and multi-band spectral subtraction for digital cameras

This paper proposes a new non-stationary noise suppression method to reduce the mechanical noise generated when audio signals are recorded with a digital camera. The proposed method first utilizes a non-negative matrix factorization (NMF) technique to estimate the noise spectrum of the mechanical noise from the noisy audio spectrum. After that, the mechanical noise contaminated in the audio signal is suppressed by multi-band spectral subtraction. In particular, the NMF technique estimates the noise spectrum in a frame-wise manner in order for the proposed method to operate in real-time. The performance of the proposed mechanical noise suppression method is evaluated in terms of log-spectral distortion, cepstral distortion, subjective quality, and computational complexity. In addition, it is compared with the performance of conventional methods. It is shown from the evaluation that the proposed method provides lower log-spectral and cepstral distortions with better subjective preference than conventional methods. Moreover, the complexity of the proposed method is low enough to be implemented on a commercially available digital camera.

선박 잡음 환경에서의 강건한 음성 인식 기반 육해상 통합 관광 정보 안내 시스템

본 논문에서는 선박에서의 잡음 환경에 강건한 음성인식 기술을 포함하는 육해상 통합관광정보 안내 시스템을 제안한다. 대부분의 음성인식 전처리부는 차량, 배블(babble) 잡음 등의 정상특성 잡음 제거하기 위해 위너(Wiener) 필터를 이용해 왔다. 하지만 이러한 기존의 전처리부는 항해중인 선박 내에서 발생하는 비정상 잡음을 제거하는데 한계가 있다. 이러한 한계를 극복하기 위해 제안하는 시스템은 높은 관광 경로 인식 정확성을 얻기 위해 비선형 다중밴드 스펙트럴 차감법(multi-band spectral subtraction)을 적용한다. 실험 결과 제안된 시스템은 기존 대비 10 dB 신호대잡음비의 잡음 환경에서 평균 5.54%의 경로명 인식률 개선을 보였다. In this paper, a land and maritime unified tourism information guide system is proposed which employs robust speech recognition in ship noise environments. Most of conventional front-ends for speech recognition have used a Wiener filter to compensate for stationary noise such as car or babble noises. However, such the conventional front-ends have limitation in reducing non-stationary noise that are occurred inside the ship on voyage. To overcome such a limitation, the proposed system incorporates nonlinear multi-band spectral subtraction to provide highly accurate tourism route recognition. It is shown from the experiment that compared to a conventional system the proposed system achieves relative improvement of a tourism route recognition rate by 5.54% under a noise condition of 10 dB signal-to-noise ratio (SNR).

A Multi-Band Speech Enhancement Algorithm Exploiting Iterative Processing for Enhancement of Single Channel Speech

This paper proposes a multi-band speech enhancement algorithm exploiting iterative processing for enhancement of single channel speech. In the proposed algorithm, the output of the multi-band spectral subtraction (MBSS) algorithm is used as the input signal again for next iteration process. As after the first MBSS processing step, the additive noise transforms to the remnant noise, the remnant noise needs to be further re-estimated. The proposed algorithm reduces the remnant musical noise further by iterating the enhanced output signal to the input again and performing the operation repeatedly. The newly estimated remnant noise is further used to process the next MBSS step. This procedure is iterated a small number of times. The proposed algorithm estimates noise in each iteration and spectral over-subtraction is executed independently in each band. The experiments are conducted for various types of noises. The performance of the proposed enhancement algorithm is evaluated for various types of noises at different level of SNRs using, 1) objective quality measures: signal-to-noise ratio (SNR), segmental SNR, perceptual evaluation of speech quality (PESQ); and 2) subjective quality measure: mean opinion score (MOS). The results of proposed enhancement algorithm are compared with the popular MBSS algorithm. Experimental results as well as the objective and subjective quality measurement test results confirm that the enhanced speech obtained from the proposed algorithm is more pleasant to listeners than speech enhanced by classical MBSS algorithm.

An Improved Multi-Band Spectral Subtraction Algorithm for Enhancing Speech in Various Noise Environments

This paper proposes an improved multi-band spectral subtraction algorithm with the goal of improving the quality of speech signal in various noise environments. In the proposed enhancement algorithm, the whole speech spectrum is divided into different uniformly spaced continuous frequency bands and spectral over-subtraction is performed in each band, independently. The proposed algorithm uses a novel approach to estimate the noise from each band continuously, without using speech pause detection. The noise is estimated and updated by adaptively smoothing the noisy signal power in each uniformly spaced frequency band. The smoothing parameter is controlled by a linear function of a-posteriori signal-to-noise ratio (SNR). The experiments are conducted for various types of noises and the results of proposed algorithm are compared with the reference multi-band spectral subtraction algorithm. To test the performance of the proposed speech enhancement algorithm, objective quality measurement tests (SNR, segmental SNR (Seg.SNR), and perceptual evaluation of speech quality (PESQ)) and spectrogram with informal listening tests are conducted for various noise types at different SNRs. Experimental results and objective quality evaluation test results confirmed the performance of proposed enhancement algorithm. The proposed enhancement algorithm provides sufficient noise reduction and good perceptual quality, without causing considerable signal distortion and remnant musical noise.

Single-Channel Speech Enhancement Using Critical-Band Rate Scale Based Improved Multi-Band Spectral Subtraction

This paper addresses the problem of single-channel speech enhancement in the adverse environment. The critical-band rate scale based on improved multi-band spectral subtraction is investigated in this study for enhancement of single-channel speech. In this work, the whole speech spectrum is divided into different non-uniformly spaced frequency bands in accordance with the critical-band rate scale of the psycho-acoustic model and the spectral over-subtraction is carried-out separately in each band. In addition, for the estimation of the noise from each band, the adaptive noise estimation approach is used and does not require explicit speech silence detection. The noise is estimated and updated by adaptively smoothing the noisy signal power in each band. The smoothing parameter is controlled by a-posteriori signal-to-noise ratio (SNR). For the performance analysis of the proposed algorithm, the objective measures, such as, SNR, segmental SNR, and perceptual evaluations of the speech quality are conducted for the variety of noises at different levels of SNRs. The speech spectrogram and objective evaluations of the proposed algorithm are compared with other standard speech enhancement algorithms and proved that the musical structure of the remnant noise and background noise is better suppressed by the proposed algorithm.

A modified weighted overlap and add-based spectral subtraction method

The Weighted Overlap and Add (WOLA) method is an optimization of frequency modulated DFT filter banks. However, the decrement in frequency resolution of speech signal caused by WOLA leads to noise estimation error. In this study, a speech power compression and gain compensation method was proposed to solve the low-resolution problem from the WOLA process. Finally, a multi-band spectral subtraction method was developed by combining the WOLA and the spectral subtraction algorithm in frequency domain. The noise reduction degrees were determined by the individual Signal-to-Noise Ratios (SNRs) in each frequency bands. Objective evaluation of the proposed noise reduction method was performed based on Itakura-Saito Distortion Measure under different types of noise. Statistical results showed that this proposed method could improve noisy speech by 5 dB SNR with little distortion. This noise reduction method has an advantage on low computation load, real-time processing and good performance on noise reduction. So this method can be implemented in embedded devices, e.g. hearing aid and cochlear implants. # This work is supported by National Natural Science Foundation of China (11104316). and Shanghai Natural Science Foundation (11ZR1446000).

Dual-channel spectral subtraction algorithms based speech enhancement dedicated to a bilateral cochlear implant

In this paper, two speech enhancement algorithms (SEAs) based on spectral subtraction (SS) principle have been evaluated for bilateral cochlear implant (BCI) users. Specifically, dual-channel noise power spectral estimation algorithm using power spectral densities (PSD) and cross power spectral density (CPSD) of the observed signals was studied. The enhanced speech signals were obtained using either Dual Channel Non Linear Spectral Subtraction ‘DC-NLSS’ or Dual-Channel Multi-Band Spectral Subtraction ‘DC-MBSS’ algorithms. For performance evaluation, some objective speech assessment tests relying on Perceptual Evaluation of Speech Quality (PESQ) score and speech Itakura-Saito (IS) distortion measurement were performed to fix the optimal number of frequency band needed in DC-MBSS algorithm. In order to evaluate the speech intelligibility, subjective listening tests were assessed with 50 normal hearing listeners using a specific BCI simulator and with three deafened BCI patients. Experimental results, obtained using French Lafon database corrupted by an additive babble noise at different Signal-to-Noise Ratios (SNR), showed that DC-MBSS algorithm improves speech understanding better than DC-NLSS algorithm for single and multiple interfering noise sources.

SPEECH ENHANCEMENT FOR ROBUST SPEECH RECOGNITION IN MOTORCYCLE ENVIRONMENT

In the present work, we investigate the performance of a number of traditional and recent speech enhancement algorithms in the adverse non-stationary conditions, which are distinctive for motorcycles on the move. The performance of these algorithms is ranked in terms of the improvement they contribute to the speech recognition accuracy, when compared to the baseline performance, i.e. without speech enhancement. The experiments on the MoveOn motorcycle speech and noise database indicated that there is no equivalence between the ranking of algorithms based on the human perception of speech quality and the speech recognition performance. The Multi-band spectral subtraction method was observed to lead to the highest speech recognition performance.

The Application of Nonlinear Spectral Subtraction Method on Millimeter Wave Conducted Speech Enhancement

A nonlinear multiband spectral subtraction method is investigated in this study to reduce the colored electronic noise in millimeter wave (MMW) radar conducted speech. Because the over-subtraction factor of each Bark frequency band can be adaptively adjusted, the nonuniform effects of colored noise in the spectrum of the MMW radar speech can be taken into account in the enhancement process. Both the results of the time-frequency distribution analysis and perceptual evaluation test suggest that a better whole-frequency noise reduction effect is obtained, and the perceptually annoying musical noise was efficiently reduced, with little distortion to speech information as compared to the other standard speech enhancement algorithm.

Formant Tracking Linear Prediction Model using HMMs for Noisy Speech Processing

This paper presents a formant-tracking linear prediction (FTLP) model for speech processing in noise. The main focus of this work is the detection of formant trajectory based on Hidden Markov Models (HMM), for improved formant estimation in noise. The approach proposed in this paper provides a systematic framework for modelling and utilization of a timesequence of peaks which satisfies continuity constraints on parameter; the within peaks are modelled by the LP parameters. The formant tracking LP model estimation is composed of three stages: (1) a pre-cleaning multi-band spectral subtraction stage to reduce the effect of residue noise on formants (2) estimation stage where an initial estimate of the LP model of speech for each frame is obtained (3) a formant classification using probability models of formants and Viterbi-decoders. The evaluation results for the estimation of the formant tracking LP model tested in Gaussian white noise background, demonstrate that the proposed combination of the initial noise reduction stage with formant tracking and LPC variable order analysis, results in a significant reduction in errors and distortions. The performance was evaluated with noisy natual vowels extracted from international french and English vocabulary speech signals at SNR value of 10dB. In each case, the estimated formants are compared to reference formants. Keywords—Formants Estimation, HMM, Multi Band Spectral Subtraction, Variable order LPC coding, White Gauusien Noise.

Statistical Model-Based VAD Algorithm with Wavelet Transform

This paper presents a new statistical model-based voice activity detection (VAD) algorithm in the wavelet domain to improve the performance in non-stationary environments. Due to the efficient time-frequency localization and the multi-resolution characteristics of the wavelet representations, the wavelet transforms are quite suitable for processing non-stationary signals such as speech. To utilize the fact that the wavelet packet is very efficient approximation of discrete Fourier transform and has built-in de-noising capability, we first apply wavelet packet decomposition to effectively localize the energy in frequency space, use spectral subtraction, and employ matched filtering to enhance the SNR. Since the conventional wavelet-based spectral subtraction eliminates the low-power speech signal in onset and offset regions and generates musical noise, we derive an improved multi-band spectral subtraction. On the other hand, noticing that fixed threshold cannot follow fluctuations of time varying noise power and the inability to adapt to a time-varying environment severely limits the VAD performance, we propose a statistical model-based VAD algorithm in wavelet domain with an adaptive threshold. We perform extensive computer simulations and compare with the conventional algorithms to demonstrate performance improvement of the proposed algorithm under various noise environments.