Speech enhancement based on the subspace method

Asano, Futoshi; Hayamizu, Satoru; Yamada, Takeshi; Nakamura, Satoshi

doi:10.1109/89.861364

Cited by 103 publications

(40 citation statements)

References 13 publications

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“…This joint diagonalization was first used [21], [22], [26], [29] in the singlechannel case. In our multichannel context as shown in [30], [31], we have…”

Section: Subspace Methodsmentioning

confidence: 99%

Analysis and Comparison of Multichannel Noise Reduction Methods in a Common Framework

Huang¹,

Benesty

Chen³

2008

IEEE Trans. Audio Speech Lang. Process.

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Abstract-Noise reduction for speech enhancement is a useful technique, but in general it is a challenging problem. While a single-channel algorithm is easy to use in practice, it inevitably introduces speech distortion to the desired speech signal while reducing noise. Today, the explosive growth in computational power and the continuous drop in the cost and size of acoustic electric transducers are driving the interest of employing multiple microphones in speech processing systems. This opens new opportunities for noise reduction. In this paper, we present an analysis of three multichannel noise reduction algorithms, namely Wiener filter, subspace, and spatial-temporal prediction, in a common framework. We intend to investigate whether it is possible for the multichannel noise reduction algorithms to reduce noise without speech distortion. Finally, we justify what we learn via theoretical analyses by simulations using real impulse responses measured in the varechoic chamber at Bell Labs.Index Terms-Microphone array signal processing, multichannel subspace method, multichannel Wiener filter, noise reduction, spatial prediction, speech enhancement.

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“…This joint diagonalization was first used [21], [22], [26], [29] in the singlechannel case. In our multichannel context as shown in [30], [31], we have…”

Section: Subspace Methodsmentioning

confidence: 99%

Analysis and Comparison of Multichannel Noise Reduction Methods in a Common Framework

Huang¹,

Benesty

Chen³

2008

IEEE Trans. Audio Speech Lang. Process.

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“…A first approach consists in attempting to recover an "enhanced" speech signal close to the anechoic one by processing its reverberated version while keeping the acoustic models unchanged. These methods may be further categorized into single-microphone methods [5][6][7][8][9] and multimicrophone methods [10][11][12][13][14]. While these methods can produce high quality enhanced speech, they are generally computationally demanding and may require solving ill-conditioned mathematical problems [15,16].…”

Section: Introductionmentioning

confidence: 99%

Blind Model Selection for Automatic Speech Recognition in Reverberant Environments

Couvreur

2004

Real World Speech Processing

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Abstract. This communication presents a new method for automatic speech recognition in reverberant environments. Our approach consists in the selection of the best acoustic model out of a library of models trained on artificially reverberated speech databases corresponding to various reverberant conditions. Given a speech utterance recorded within a reverberant room, a Maximum Likelihood estimate of the fullband room reverberation time is computed using a statistical model for short-term log-energy sequences of anechoic speech. The estimated reverberation time is then used to select the best acoustic model, i.e., the model trained on a speech database most closely matching the estimated reverberation time, which serves to recognize the reverberated speech utterance. The proposed model selection approach is shown to improve significantly recognition accuracy for a connected digit task in both simulated and real reverberant environments, outperforming standard channel normalization techniques.

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“…However, is unobservable; as a result, an estimation of may seem difficult to obtain. But (10) Now depends on the correlation vectors and . The vector (which is also the first column of ) can be easily estimated during speech and noise periods while can be estimated during noise-only intervals assuming that the statistics of the noise do not change much with time.…”

Section: Estimation Of the Clean Speech Samplesmentioning

confidence: 99%

“…Using (10) and the fact that , we obtain the optimal filter (11) where (12) is the signal-to-noise ratio, is the identity matrix, and…”

Section: Estimation Of the Clean Speech Samplesmentioning

confidence: 99%

New insights into the noise reduction Wiener filter

Chen¹,

Benesty

Huang³

et al. 2006

IEEE Trans. Audio Speech Lang. Process.

530

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Abstract-The problem of noise reduction has attracted a considerable amount of research attention over the past several decades. Among the numerous techniques that were developed, the optimal Wiener filter can be considered as one of the most fundamental noise reduction approaches, which has been delineated in different forms and adopted in various applications. Although it is not a secret that the Wiener filter may cause some detrimental effects to the speech signal (appreciable or even significant degradation in quality or intelligibility), few efforts have been reported to show the inherent relationship between noise reduction and speech distortion. By defining a speech-distortion index to measure the degree to which the speech signal is deformed and two noise-reduction factors to quantify the amount of noise being attenuated, this paper studies the quantitative performance behavior of the Wiener filter in the context of noise reduction. We show that in the single-channel case the a posteriori signal-to-noise ratio (SNR) (defined after the Wiener filter) is greater than or equal to the a priori SNR (defined before the Wiener filter), indicating that the Wiener filter is always able to achieve noise reduction. However, the amount of noise reduction is in general proportional to the amount of speech degradation. This may seem discouraging as we always expect an algorithm to have maximal noise reduction without much speech distortion. Fortunately, we show that speech distortion can be better managed in three different ways. If we have some a priori knowledge (such as the linear prediction coefficients) of the clean speech signal, this a priori knowledge can be exploited to achieve noise reduction while maintaining a low level of speech distortion. When no a priori knowledge is available, we can still achieve a better control of noise reduction and speech distortion by properly manipulating the Wiener filter, resulting in a suboptimal Wiener filter. In case that we have multiple microphone sensors, the multiple observations of the speech signal can be used to reduce noise with less or even no speech distortion.

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Speech enhancement based on the subspace method

Cited by 103 publications

References 13 publications

Analysis and Comparison of Multichannel Noise Reduction Methods in a Common Framework

Analysis and Comparison of Multichannel Noise Reduction Methods in a Common Framework

Blind Model Selection for Automatic Speech Recognition in Reverberant Environments

New insights into the noise reduction Wiener filter

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