Spatially selective sound capture for speech and audio processing

Flanagan, James L.; Surendran, Arun C.; Jan, E. E.

doi:10.1016/0167-6393(93)90072-s

Cited by 86 publications

(48 citation statements)

References 8 publications

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“…Consequently, we utilize frequency-weighted segmental signal-to-noise ratio [32] to measure performance, which takes into account of phase information. Specifically, (14) where is the original noise-and reverberation-free signal, and is the processed signal. is the end-time of the th frame and the summation is over frames, each of length (we use a length of 30 ms).…”

Section: Results and Comparisonsmentioning

confidence: 99%

“…Some enhancement algorithms assume that room impulse response functions are known. For instance, delay-sum beamformers [13] and matched filters [14] have been employed to reduce reverberation effects. One idea to remove reverberation effects is by passing the reverberant signal through a second filter that inverts the reverberation process and recover the original signal.…”

Section: Introductionmentioning

confidence: 99%

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A two-stage algorithm for one-microphone reverberant speech enhancement

Wang

2006

IEEE Trans. Audio Speech Lang. Process.

162

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Abstract-Under noise-free conditions, the quality of reverberant speech is dependent on two distinct perceptual components: coloration and long-term reverberation. They correspond to two physical variables: signal-to-reverberant energy ratio (SRR) and reverberation time, respectively. Inspired by this observation, we propose a two-stage reverberant speech enhancement algorithm using one microphone. In the first stage, an inverse filter is estimated to reduce coloration effects or increase SRR. The second stage employs spectral subtraction to minimize the influence of long-term reverberation. The proposed algorithm significantly improves the quality of reverberant speech. A comparison with a recent enhancement algorithm is made on a corpus of speech utterances in a number of reverberant conditions, and the results show that our algorithm performs substantially better.

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Section: Results and Comparisonsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A two-stage algorithm for one-microphone reverberant speech enhancement

Wang

2006

IEEE Trans. Audio Speech Lang. Process.

162

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“…Under the aegis of the National Science Foundation, the CAIP Center is examining possibilities for high-quality selective sound capture, bounded in three-dimensions, using large three-dimensional arrays of microphones. Signal processing to produce multiple beamforming (on the sound source and its major multipath images) leads to significant gains (Figures 9.9 and 9.10; Flanagan, Surendran, et al, 1993).…”

Section: Integration Of Multiple Modalitiesmentioning

confidence: 99%

“…Equivalently, matched filtering applied to every sensor of the array provides spatial volume selectivity and mitigates reverberant distortion and interference by competing sound sources (Flanagan, Surendran, et al, 1993).…”

Section: Integration Of Multiple Modalitiesmentioning

confidence: 99%

Survey of the State of the Art in Human Language Technology

Cohen¹,

Varile²,

Zampolli³

et al. 2000

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133

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“…The first of these three directions has evolved into a separate branch of acoustics called architectural acoustics (e.g., [3], [4]). Example studies for the second and the third directions mentioned are [5], [6], and [7], [8], respectively. The reverberation properties of a room play an extremely important role in determining the listening experience.…”

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confidence: 99%

Fast Evaluation of the Room Transfer Function Using Multipole Expansion

Duraiswami

Zotkin

Gumerov

2007

IEEE Trans. Audio Speech Lang. Process.

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Abstract-Reverberation in rooms is often simulated with the image method due to Allen and Berkley (1979). This method has an asymptotic complexity that is cubic in terms of the simulated reverberation length. When employed in the frequency domain, it is relatively computationally expensive if there are many receivers in the room or if the source or receiver positions are changing with time. The computational complexity of the image method is due to the repeated summation of the fields generated by a large number of image sources. In this paper, a fast method to perform such summations is presented. The method is based on multipole expansion of the monopole source potential. For offline computation of the room transfer function for image sources and receiver points, use of the Allen-Berkley algorithm requires ( ) operations, whereas use of the proposed method requires only ( + ) operations, resulting in significantly faster computation of reverberant sound fields. The proposed method also has a considerable speed advantage in situations where the room transfer function must be rapidly updated online in response to source/receiver location changes. Simulation results are presented, and algorithm accuracy, speed, and implementation details are discussed. For problems that require frequency-domain computations, the algorithm is found to generate sound fields identical to the ones obtained with the frequency-domain version of the Allen-Berkley algorithm at a fraction of computational cost.

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Spatially selective sound capture for speech and audio processing

Cited by 86 publications

References 8 publications

A two-stage algorithm for one-microphone reverberant speech enhancement

A two-stage algorithm for one-microphone reverberant speech enhancement

Survey of the State of the Art in Human Language Technology

Fast Evaluation of the Room Transfer Function Using Multipole Expansion

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