Multi-Microphone Noise Reduction Based on Orthogonal Noise Signal Decompositions

Habets, Emanuël A. P.; Benesty, Jacob

doi:10.1109/tasl.2013.2244086

Cited by 12 publications

(5 citation statements)

References 8 publications

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“…In this experiment the noise reduction performance of two fixed beamformers (delay-and-sum (19) and MVDR (18)) is compared for stationary diffuse noise at a global input SNR of 5 dB. For the delay-and-sum beamformer, a delay in (14) 2) As expected, the MVDR beamformer performs better than the delay-and-sum beamformer by using a better model for the acoustic transfer function and the noise coherence matrix.…”

Section: B Noise Reduction Performance Of the Fixed Beamformermentioning

confidence: 76%

“…In recent years some perceptually motivated beamformers have been proposed, which allow a tradeoff between noise reduction and dereverberation [15], between the reduction of different noise types [19], or incorporating spatial cue preservation [22]. For instance, in [19], the noise correlation matrix, which is assumed to be known, is decomposed into coherent and incoherent parts.…”

Section: A Beamforming and Blocking Matricesmentioning

confidence: 99%

“…For instance, in [19], the noise correlation matrix, which is assumed to be known, is decomposed into coherent and incoherent parts. The decomposed noise correlation matrices are then used to derive a beamformer that provides a better control between spatially coherent and incoherent noise reduction.…”

Section: A Beamforming and Blocking Matricesmentioning

confidence: 99%

See 2 more Smart Citations

Noise Power Spectral Density Estimation Using MaxNSR Blocking Matrix

Wang

Gerkmann

Doclo

2015

IEEE/ACM Trans. Audio Speech Lang. Process.

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In this paper a multi-microphone noise reduction system based on the generalized sidelobe canceller (GSC) structure is investigated. The system consists of a fixed beamformer providing an enhanced speech reference, a blocking matrix providing a noise reference by suppressing the target speech, and a single-channel spectral postfilter. The spectral post-filter requires the power spectral density (PSD) of the residual noise in the speech reference, which can in principle be estimated from the PSD of the noise reference. However, due to speech leakage in the noise reference, the noise PSD is overestimated, leading to target speech distortion. To minimize the influence of the speech leakage, a maximum noise-to-speech ratio (MaxNSR) blocking matrix is proposed, which maximizes the ratio between the noise and the speech leakage in the noise reference. The proposed blocking matrix can be computed from the generalized eigenvalue decomposition of the correlation matrix of the microphone signals and the noise coherence matrix, which is assumed to be time-invariant. Experimental results in both stationary and nonstationary diffuse noise fields show that the proposed algorithm outperforms existing blocking matrices in terms of target speech blocking ability, noise estimation and noise reduction performance.

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Section: B Noise Reduction Performance Of the Fixed Beamformermentioning

confidence: 76%

Section: A Beamforming and Blocking Matricesmentioning

confidence: 99%

See 1 more Smart Citation

Noise Power Spectral Density Estimation Using MaxNSR Blocking Matrix

Wang

Gerkmann

Doclo

2015

IEEE/ACM Trans. Audio Speech Lang. Process.

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“…Localization is accomplished by exploiting specific features of the ray space, in particular through (10). From , peaks are identified for each row i,: at values m ˆ i .…”

Section: Relation With the Soundfield Mapmentioning

confidence: 99%

The Ray Space Transform: A New Framework for Wave Field Processing

Bianchi

Antonacci

Sarti

et al. 2016

IEEE Trans. Signal Process.

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Soundfield imaging is a special analysis methodology aimed at capturing the directional components of the acoustic field and mapping them onto a domain called "ray space", where rele-vant acoustic objects become linear patterns, i.e., sets of collinear points. This allows us to overcome resolution issues while easing far-field assumptions. In this paper, we generalize this concept by introducing the ray space transform for acoustic field repre-sentation. The transform is based on a short space-time Fourier transform of the signals captured by a microphone array, using discrete Gabor frames. The resulting transform coefficients are parameterized in the same ray space used for soundfield imaging. The resulting transform enables the definition of analysis and syn-thesis operators, which exhibit perfect reconstruction capabilities. We show examples of applications of the ray space transform to source localization and spot spatial filtering.

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“…Digital signal modulation recognition technology serves as a critical component in communication signal reconnaissance, acting as the foundation and basis for signal demodulation [3][4]. In recent years, modulation methods and communication channels have become increasingly complex, posing new challenges to digital signal modulation recognition technology [5]. Therefore, researching the technology for recognizing digital signal modulation methods holds important research significance.…”

Section: Introductionmentioning

confidence: 99%

Modulation Signal Recognition Based on Endpoint Detection and Cepstral Parameters

Xiuquan,

Zhen,

Yeyin

et al. 2024

Preprint

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Digital signal modulation recognition technology serves as the foundation and basis for signal demodulation, playing a crucial role in communication signal reconnaissance and holding significant research significance. This paper conducts research on digital signal modulation recognition technology from the perspectives of signal preprocessing and feature extraction. Seven modulation signals, namely 2ASK, 4ASK, 2FSK, 4FSK, 2PSK, 4PSK, and OFDM, are selected as recognition targets. The paper compares the effects of four different endpoint detection algorithms on modulation signal recognition. The results indicate that, for these seven modulation signals, the short-time energy entropy ratio algorithm performs the best, achieving a correct endpoint detection rate of over 93% in a Gaussian channel with a signal-to-noise(SNR) ratio of 0dB. Based on this, three different denoising algorithms are introduced to further enhance the performance of the short-time energy entropy ratio algorithm. The results show that the wavelet denoising algorithm achieves the greatest improvement in the performance of the short-time energy entropy ratio algorithm, with a short processing time. In a Gaussian channel with a SNR ratio greater than − 10dB, the endpoint detection accuracy of this algorithm can be maintained at over 95%. Finally, for the accurate identification and differentiation of 2FSK and 4FSK, this paper optimized the relevant algorithm in the cyclic spectrum. The kurtosis coefficient value Kur of the cyclic spectrum parameter matrix at the cyclic frequency \(\alpha =0\) is utilized to distinguish between these two signals. The results show that, at a SNR ratio of 4dB, the modulation recognition algorithm proposed in this paper can effectively distinguish between these two signals, achieving a recognition accuracy of over 99%.

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Multi-Microphone Noise Reduction Based on Orthogonal Noise Signal Decompositions

Cited by 12 publications

References 8 publications

Noise Power Spectral Density Estimation Using MaxNSR Blocking Matrix

Noise Power Spectral Density Estimation Using MaxNSR Blocking Matrix

The Ray Space Transform: A New Framework for Wave Field Processing

Modulation Signal Recognition Based on Endpoint Detection and Cepstral Parameters

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