Noise reduction for dual-microphone mobile phones exploiting power level differences

Jeub, Marco; Herglotz, Christian; Nelke, Christoph Matthias; Beaugeant, Christophe; Vary, Peter

doi:10.1109/icassp.2012.6288223

Cited by 68 publications

(50 citation statements)

References 6 publications

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“…The second estimator is the coherence-based signal-todiffuse ratio estimator (CSDRE) [32]; a similar estimator is also presented in [33]. It calculates the SDR in mixed sound fields by exploiting the spatial coherence of a single directional component and the diffuse sound field.…”

Section: Based On the Spatial Coherencementioning

confidence: 99%

A multichannel diffuse power estimator for dereverberation in the presence of multiple sources

Braun

Habets

2015

J AUDIO SPEECH MUSIC PROC.

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Using a recently proposed informed spatial filter, it is possible to effectively and robustly reduce reverberation from speech signals captured in noisy environments using multiple microphones. Late reverberation can be modeled by a diffuse sound field with a time-varying power spectral density (PSD). To attain reverberation reduction using this spatial filter, an accurate estimate of the diffuse sound PSD is required. In this work, a method is proposed to estimate the diffuse sound PSD from a set of reference signals by blocking the direct signal components. By considering multiple plane waves in the signal model to describe the direct sound, the method is suitable in the presence of multiple simultaneously active speakers. The proposed diffuse sound PSD estimator is analyzed and compared to existing estimators. In addition, the performance of the spatial filter computed with the diffuse sound PSD estimate is analyzed using simulated and measured room impulse responses in noisy environments with stationary noise and non-stationary babble noise.

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Section: Based On the Spatial Coherencementioning

confidence: 99%

A multichannel diffuse power estimator for dereverberation in the presence of multiple sources

Braun

Habets

2015

J AUDIO SPEECH MUSIC PROC.

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“…This allows for selecting channels within the dynamic signal combination unit without getting switching artifacts. The spectral alignment to a reference noise spectrum is done by dynamic modification of a frequency-dependent spectral floor parameter within the noise reduction (16). The computation of this dynamic spectral floor is proposed in (32), where we need to know an appropriate reference noise PSD.…”

Section: Reference Noise Power Spectral Density Estimationmentioning

confidence: 99%

“…The latter combines a subset of some speakerrelated microphone signals to a particular output signal. The whole signal processing is controlled by a control unit based on the comparison of signal powers (see also [11,16]). …”

Section: Introductionmentioning

confidence: 99%

A dynamic multi-channel speech enhancement system for distributed microphones in a car environment

Matheja¹,

Buck²,

Fingscheidt

2013

EURASIP J. Adv. Signal Process.

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Supporting multiple active speakers in automotive hands-free or speech dialog applications is an interesting issue not least due to comfort reasons. Therefore, a multi-channel system for enhancement of speech signals captured by distributed distant microphones in a car environment is presented. Each of the potential speakers in the car has a dedicated directional microphone close to his position that captures the corresponding speech signal. The aim of the resulting overall system is twofold: On the one hand, a combination of an arbitrary pre-defined subset of speakers' signals can be performed, e.g., to create an output signal in a hands-free telephone conference call for a far-end communication partner. On the other hand, annoying cross-talk components from interfering sound sources occurring in multiple different mixed output signals are to be eliminated, motivated by the possibility of other hands-free applications being active in parallel. The system includes several signal processing stages. A dedicated signal processing block for interfering speaker cancellation attenuates the cross-talk components of undesired speech. Further signal enhancement comprises the reduction of residual cross-talk and background noise. Subsequently, a dynamic signal combination stage merges the processed single-microphone signals to obtain appropriate mixed signals at the system output that may be passed to applications such as telephony or a speech dialog system. Based on signal power ratios between the particular microphone signals, an appropriate speaker activity detection and therewith a robust control mechanism of the whole system is presented. The proposed system may be dynamically configured and has been evaluated for a car setup with four speakers sitting in the car cabin disturbed in various noise conditions.

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“…As to reverberation, one idea to remove the negative effects is to pass the reverberant signal through a second filter that inverts the reverberation process and recovers the original signal. Jeub et al presented a novel two-stage binaural dereverberation algorithm which consists of the model of the room impulse response (RIR) and a dual-channel Wiener filter to preserve the binaural cues [18]. Benesty et al provided a multichannel widely linear approach to deal with the noise reduction of binaural signal [19].…”

Section: Introductionmentioning

confidence: 99%

Binaural sound source localization based on generalized parametric model and two-layer matching strategy in complex environments

Liu

Pang

Zhang

2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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Binaural sound source localization is an important technique involving Human-Robot Interaction (HRI), video conference, speech enhancement, etc. In many real application scenarios, especially for closed environments, the affect of reverberation and noise would degrade the precision of position estimations. Therefore, a new binaural sound source localization method based on generalized parametric model and two-layer matching strategy is proposed in this paper for complex environments. Firstly, cepstral prefiltering is utilized for dereverberation of binaural signals. Then, two binaural cues computed from a dual-channel frequency representation, are combined to estimate the azimuths of sources. Additionally, the generalized parametric model is presented to describe the relationship between the azimuth and binaural cues through finding the optimal scaling factors from training data. At last, a two-layer matching strategy based on Bayesian rule is used to make the final decision, which can effectively decrease the computation complexity. Experiments have validated the proposed approach and show that it achieves favorably better results compared with several available methods without extra spacial burden.

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Noise reduction for dual-microphone mobile phones exploiting power level differences

Cited by 68 publications

References 6 publications

A multichannel diffuse power estimator for dereverberation in the presence of multiple sources

A multichannel diffuse power estimator for dereverberation in the presence of multiple sources

A dynamic multi-channel speech enhancement system for distributed microphones in a car environment

Binaural sound source localization based on generalized parametric model and two-layer matching strategy in complex environments

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