Musical Noise Research Articles

Improved Noise Minimum Statistics Estimation Algorithm for Using in a Speech-Passing Noise-Rejecting Headset

This paper deals with configuration of an algorithm to be used in a speech-passing angle grinder noise-canceling headset. Angle grinder noise is annoying and interrupts ordinary oral communication. Meaning that, low SNR noisy condition is ahead. Since variation in angle grinder working condition changes noise statistics, the noise will be nonstationary with possible jumps in its power. Studies are conducted for picking an appropriate algorithm. A modified version of the well-known spectral subtraction shows superior performance against alternate methods. Noise estimation is calculated through a multi-band fast adapting scheme. The algorithm is adapted very quickly to the non-stationary noise environment while inflecting minimum musical noise and speech distortion on the processed signal. Objective and subjective measures illustrating the performance of the proposed method are introduced.

Musical-Noise Analysis in Methods of Integrating Microphone Array and Spectral Subtraction Based on Higher-Order Statistics

We conduct an objective analysis on musical noise generated by two methods of integrating microphone array signal processing and spectral subtraction. To obtain better noise reduction, methods of integrating microphone array signal processing and nonlinear signal processing have been researched. However, nonlinear signal processing often generates musical noise. Since such musical noise causes discomfort to users, it is desirable that musical noise is mitigated. Moreover, it has been recently reported that higherorder statistics are strongly related to the amount of musical noise generated. This implies that it is possible to optimize the integration method from the viewpoint of not only noise reduction performance but also the amount of musical noise generated. Thus, we analyze the simplest methods of integration, that is, the delay-and-sum beamformer and spectral subtraction, and fully clarify the features of musical noise generated by each method. As a result, it is clarified that a specific structure of integration is preferable from the viewpoint of the amount of generated musical noise. The validity of the analysis is shown via a computer simulation and a subjective evaluation.

Perceptual improvement of Wiener filtering employing a post-filter

A major drawback of many speech enhancement methods in speech applications is the generation of an annoying residual noise with musical character. Although the Wiener filter introduces less musical noise than spectral subtraction methods, such noise, however, exists and is perceptually annoying to the listener. A potential solution to this artifact is the incorporation of a psychoacoustic model in the suppression filter design. In this paper a frequency domain optimal linear estimator with perceptual post-filtering is proposed, which incorporates the masking properties of the human hearing system to render the residual noise distortion inaudible. Proposed post-processing presents a modified way to measure the tonality coefficient and relative threshold offset for an optimal estimation of the noise masking threshold. The performance of the proposed enhancement algorithm is evaluated by the segmental SNR, Modified Bark Spectral Distortion (MBSD) and Perceptual Evaluation of Speech Quality (PESQ) measures under various noisy environments and yields better results compared to the Wiener filtering based on Ephraim-Malah's decision-directed approach.

Single-channel speech enhancement using spectral subtraction in the short-time modulation domain

In this paper we investigate the modulation domain as an alternative to the acoustic domain for speech enhancement. More specifically, we wish to determine how competitive the modulation domain is for spectral subtraction as compared to the acoustic domain. For this purpose, we extend the traditional analysis-modification-synthesis framework to include modulation domain processing. We then compensate the noisy modulation spectrum for additive noise distortion by applying the spectral subtraction algorithm in the modulation domain. Using an objective speech quality measure as well as formal subjective listening tests, we show that the proposed method results in improved speech quality. Furthermore, the proposed method achieves better noise suppression than the MMSE method. In this study, the effect of modulation frame duration on speech quality of the proposed enhancement method is also investigated. The results indicate that modulation frame durations of 180–280 ms, provide a good compromise between different types of spectral distortions, namely musical noise and temporal slurring. Thus given a proper selection of modulation frame duration, the proposed modulation spectral subtraction does not suffer from musical noise artifacts typically associated with acoustic spectral subtraction. In order to achieve further improvements in speech quality, we also propose and investigate fusion of modulation spectral subtraction with the MMSE method. The fusion is performed in the short-time spectral domain by combining the magnitude spectra of the above speech enhancement algorithms. Subjective and objective evaluation of the speech enhancement fusion shows consistent speech quality improvements across input SNRs.

Iterative spectral subtraction method for millimeter-wave conducted speech enhancement

A non-air conducted speech detecting method has been developed in our laboratory by using millimeter wave radar technology. Because of the special attributes of the millimeter wave, this method may considerably extend the capabilities of traditional speech detecting methods. However, radar speech is substantially degraded by additive combined noises that include radar harmonic noise, electrocircuit noise, and ambient noise. This study, therefore, proposed an iterative spectral subtraction method which can be adaptively estimate noise spectrum at every iteration, and reduce the musical noise remained in the previous spectral subtraction process. Results from simulations as well as evaluations confirm that the proposed method satisfactorily reduces whole-frequency and musical noises and produces good speech quality.

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.

Multi-Band Spectral Subtraction Method for Electrolarynx Speech Enhancement

Although the electrolarynx (EL) provides an important means of voice reconstruction for patients who lose their vocal cords by laryngectomies, the radiated noise and additive environment noise reduce the intelligibility of the resulting EL speech. This paper proposes an improved spectrum subtract algorithm by taking into account the non-uniform effect of colored noise on the spectrum of EL speech. Since the over-subtraction factor of each frequency band can be adjusted in the enhancement process, a better noise reduction effect was obtained and the perceptually annoying musical noise was efficiently reduced, as compared to other standard speech enhancement algorithms.

A Perceptual Approach to Reduce Musical Noise Using Critical Bands Tonality Coefficients and Masking Thresholds

Traditional noise reduction techniques have the drawback of generating an annoying musical noise. A new scheme for speech enhancement in high noise environment is developed by considering human auditory system masking characteristics. The new scheme considers the masking threshold of both noisy speech and the denoised one, to detect musical noise components. To make them inaudible, they are set under the noise masking threshold. The improved signal is subjected to extensive subjective and objective tests. It is ob-served that the musical noise is appreciably reduced even at very low signal to noise ratios.

Wavelet packet entropy for millimeter wave conducted speech enhancement

A new nonacoustic sensor-based speech detecting method has been developed in our laboratory by using millimeter wave radar technology. Because of the special attributes of the millimeter wave, this method may considerably extend the capabilities of traditional speech detecting methods. However, radar speech is substantially degraded by additive combined noises that include radar harmonic noise, electrocircuit noise, and ambient noise. Therefore, this study proposes an improved spectrum subtraction algorithm that uses wavelet packet entropy analysis to detect voiced and unvoiced sections in an adaptive manner. Use of this algorithm leads to a significant reduction in the unnatural components of residual noise. Results from simulations as well as evaluations confirm that enhanced nonacoustic sensor speech is more pleasant to listeners than speech produced by traditional spectral subtraction algorithms. This suggests that the proposed method satisfactorily reduces whole-frequency and musical noises and produces good speech quality

Speech enhancement for noise‐robust speech recognition.

A modified phase opponency (MPO) model was combined with the aperiodic/periodic/pitch detector (APP) by Deshmukh et al. [JASA, 121, 3886–3898 (2006)] for speech enhancement. The MPO‐APP passes the speech‐dominated regions as is but greatly attenuates the noise‐dominated regions. Thus, the noise in nonspeech regions and in the regions between the formants are significantly reduced. Unlike many other techniques, the MPO‐APP does not introduce musical noise. However, given that the noise in formant regions is passed along with the speech signal, the enhanced signal sometimes has a shadow effect. To get rid of this shadow effect, we recently developed a preprocessor for the MPO‐APP. First, the signal to noise ratio of the signal is estimated. Second, we use an adaptive generalized spectral subtraction technique to reduce the noise in the speech dominant regions. The results show a significant improvement in the quality of the enhanced signal and, in addition, increased accuracy in automatic recognition of the enhanced signals over that obtained from the original signals or the signals enhanced without the preprocessor. [Research supported by NSF Grant IIS0703859.]

A spectral filtering method based on hybrid wiener filters for speech enhancement

It is well known that speech enhancement using spectral filtering will result in residual noise. Residual noise which is musical in nature is very annoying to human listeners. Many speech enhancement approaches assume that the transform coefficients are independent of one another and can thus be attenuated separately, thereby ignoring the correlations that exist between different time frames and within each frame. This paper, proposes a single channel speech enhancement system which exploits such correlations between the different time frames to further reduce residual noise. Unlike other 2D speech enhancement techniques which apply a post-processor after some classical algorithms such as spectral subtraction, the proposed approach uses a hybrid Wiener spectrogram filter (HWSF) for effective noise reduction, followed by a multi-blade post-processor which exploits the 2D features of the spectrogram to preserve the speech quality and to further reduce the residual noise. This results in pleasant sounding speech for human listeners. Spectrogram comparisons show that in the proposed scheme, musical noise is significantly reduced. The effectiveness of the proposed algorithm is further confirmed through objective assessments and informal subjective listening tests.

Wavelet-Based Speech Enhancement Using Time-Adapted Noise Estimation

Spectral subtraction is commonly used for speech enhancement in a single channel system because of the simplicity of its implementation. However, this algorithm introduces perceptually musical noise while suppressing the background noise. We propose a wavelet-based approach in this paper for suppressing the background noise for speech enhancement in a single channel system. The wavelet packet transform, which emulates the human auditory system, is used to decompose the noisy signal into critical bands. Wavelet thresholding is then temporally adjusted with the noise power by time-adapted noise estimation. The proposed algorithm can efficiently suppress the noise while reducing speech distortion. Experimental results, including several objective measurements, show that the proposed wavelet-based algorithm outperforms spectral subtraction and other wavelet-based denoising approaches for speech enhancement for nonstationary noise environments.

Noise reduction combining time-frequency ε-filter and M-transform

This paper introduces noise reduction combining time-frequency epsilon-filter (TF epsilon-filter) and time-frequency M-transform (TF M-transform). Musical noise is an offensive noise generated due to noise reduction in the time-frequency domain such as spectral subtraction and TF epsilon-filter. It has a deleterious effect on speech recognition. To solve the problem, M-transform is introduced. M-transform is a linear transform based on M-sequence. The method combining the time-domain epsilon-filter (TD epsilon-filter) and time-domain M-transform (TD M-transform) can reduce not only white noise but also impulse noise. Musical noise is isolated in the time-frequency domain, which is similar to impulse noise in the time domain. On these prospects, this paper aims to reduce musical noise by improving M-transform for the time-frequency domain. Noise reduction by using TD M-transform and the TD epsilon-filter is first explained to clarify its features. Then, an improved method applying M-transform to the time-frequency domain, namely TF M-transform, is described. Noise reduction combining the TF epsilon-filter and TF M-transform is also proposed. The proposed method can reduce not only high-level nonstationary noise but also musical noise. Experimental results are also given to demonstrate the performance of the proposed method.

A geometric approach to spectral subtraction

The traditional power spectral subtraction algorithm is computationally simple to implement but suffers from musical noise distortion. In addition, the subtractive rules are based on incorrect assumptions about the cross terms being zero. A new geometric approach to spectral subtraction is proposed in the present paper that addresses these shortcomings of the spectral subtraction algorithm. A method for estimating the cross terms involving the phase differences between the noisy (and clean) signals and noise is proposed. Analysis of the gain function of the proposed algorithm indicated that it possesses similar properties as the traditional MMSE algorithm. Objective evaluation of the proposed algorithm showed that it performed significantly better than the traditional spectral subtractive algorithm. Informal listening tests revealed that the proposed algorithm had no audible musical noise.

Cepstral Smoothing of Spectral Filter Gains for Speech Enhancement Without Musical Noise

Many speech enhancement algorithms that modify short-term spectral magnitudes of the noisy signal by means of adaptive spectral gain functions are plagued by annoying spectral outliers. In this letter, we propose cepstral smoothing as a solution to this problem. We show that cepstral smoothing can effectively prevent spectral peaks of short duration that may be perceived as musical noise. At the same time, cepstral smoothing preserves speech onsets, plosives, and quasi-stationary narrowband structures like voiced speech. The proposed recursive temporal smoothing is applied to higher cepstral coefficients only, excluding those representing the pitch information. As the higher cepstral coefficients describe the finer spectral structure of the Fourier spectrum, smoothing them along time prevents single coefficients of the filter function from changing excessively and independently of their neighboring bins, thus suppressing musical noise. The proposed cepstral smoothing technique is very effective in nonstationary noise.

Simultaneous Detection and Estimation Approach for Speech Enhancement

In this paper, we present a simultaneous detection and estimation approach for speech enhancement. A detector for speech presence in the short-time Fourier transform domain is combined with an estimator, which jointly minimizes a cost function that takes into account both detection and estimation errors. Cost parameters control the tradeoff between speech distortion, caused by missed detection of speech components and residual musical noise resulting from false-detection. Furthermore, a modified decision-directed a priori signal-to-noise ratio (SNR) estimation is proposed for transient-noise environments. Experimental results demonstrate the advantage of using the proposed simultaneous detection and estimation approach with the proposed a priori SNR estimator, which facilitate suppression of transient noise with a controlled level of speech distortion.

Processing Noisy Speech for Enhancement

The main objective of this paper is to provide an overview of the major techniques that have been proposed for enhancement of noisy speech. This paper also proposes a method for enhancement of noisy speech. The proposed method is motivated as an attempt to minimize the limitations of conventional spectral subtraction method for enhancement of noisy speech. The proposed method involves three steps. In the first step speech and nonspeech regions are detected from the degraded speech signal. In the second step, for each speech region, noise components are estimated from the preceding noise regions and are subtracted by conventional spectral subtraction method. In the third step, speech components are enhanced further from the spectral subtracted speech signal for reducing the musical noise, which is the artifact of spectral subtraction method. The processed speech signals from the proposed method seem to be better perceptually compared to that of the spectral subtraction method.

An improved spectral subtraction method for speech enhancement using a perceptual weighting filter

We propose an improved spectral subtraction method for reducing acoustic noise added to speech in noisy environments like helicopter cockpit or car engine. This implementation uses oversubtraction method for spectral subtraction. Residual noise can be masked by exploiting the masking properties of the human auditory system. A psychoacoustically motivated weighting filter was included to eliminate residual musical noise. Simulations showed a better quality with no distortion for the enhanced speech and the musical noise was effectively masked.

Speech Enhancement by Overweighting Gain with Nonlinear Structure in Wavelet Packet Transform

A sound quality enhancement method by overweighting gain of a nonlinear structure in a wavelet packet area is provided to restrain the generation of musical noise efficiently and ensure reliable intelligibility in an enhanced voice. A sound quality enhancement method by overweighting gain of a nonlinear structure in a wavelet packet area comprises the following steps of: generating a converting signal that a voice signal polluted by noise is converted by UWPT(Uniform Wavelet Packet Transform); calculating a relative size difference, which is an identifier for calculating a relative difference between the amount of noise existing in a sub band and the amount of a voice polluted by noise; calculating the overweighting gain of the nonlinear structure from the relative size difference; calculating a transformed time-varying gain function based on an LSL(Least-Squares Line) algorithm; and performing spectral subtraction using the transformed time-varying gain function.

A Novel Approach to a Robust a Priori SNR Estimator in Speech Enhancement

SUMMARY This paper presents a novel approach to single channel speech enhancement in noisy environments. Widely adopted noise reduction techniques based on the spectral subtraction are generally expressed as a spectral gain depending on the signal-to-noise ratio (SNR) [1]–[4]. As the estimation method of the SNR, the well-known decision-directed (DD) estimator of Ephraim and Malah efficiently is known to reduces musical noise in noise frames, but the ap rioriSNR, which is a crucial parameter of the spectral gain, follows the a posteriori SNR with a delay of one frame in speech frames [5]. Therefore, the noise suppression gain using the delayed ap rioriSNR, which is estimated by the DD algorithm matches the previous frame rather than the current one, so after noise suppression, this degrades the performance of a noise reduction during abrupt transient parts. To overcome this artifact, we propose a computationally simple but effective speech enhancement technique based on the sigmoid type function to adaptively determine the weighting factor of the DD algorithm. Actually, the proposed approach avoids the delay problem of the ap rioriSNR while maintaining the advantage of the DD algorithm. The performance of the proposed enhancement algorithm is evaluated by the objective and subjective test under various environments and yields better results compared with the conventional DD scheme based approach.