On Dealing with Sampling Rate Mismatches in Blind Source Separation and Acoustic Echo Cancellation

Robledo-Arnuncio, Enrique; Wada, Ted S.; Juang, Biing‐Hwang

doi:10.1109/aspaa.2007.4393044

Cited by 33 publications

(10 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this paper, we confine ourselves to the review of optimal distributed minimum-variance BF algorithms where nodes share (compressed) signals and parameters, and where the general aim is to achieve the same speech enhancement performance as obtained with a centralized minimum-variance BF. We mainly focus on the BF algorithm design challenges, and we disregard several other (but equally important) challenges, such as synchronization [29]- [32], node subset selection [33], [34], topology selection, distortion due to audio compression [22], [35], [36], packet loss, input-output delay management [37], etc.…”

Section: Introductionmentioning

confidence: 99%

Optimal distributed minimum-variance beamforming approaches for speech enhancement in wireless acoustic sensor networks

et al. 2015

View full text Add to dashboard Cite

In multiple speaker scenarios, the so-called linearly constrained minimum variance (LCMV) beamformer is a popular microphone array-based speech enhancement technique, as it allows minimizing the noise power while maintaining a set of desired responses towards the different speakers. In this paper, we address the algorithmic challenges arising in the application of the LCMV beamformer in so-called wireless acoustic sensor networks (WASNs), which are a next-generation technology for audio acquisition and processing. We review three optimal distributed LCMV-based algorithms, which compute a networkwide LCMV beamformer output at each node without centralizing the microphone signals. Optimality here refers to the fact that the algorithms theoretically generate the same beamformer outputs as in a centralized realization where a single processor would have access to all the signals. We derive and motivate the algorithms in an accessible top-down framework that reveals the underlying relations between them, as well as their differences. We explain how these differences result from their different design criterion (node-specific versus common constraints sets), as well as their different priorities with respect to communication bandwidth, computational power, and adaptivity. Furthermore, although the three algorithms were originally proposed for a fully-connected WASN, we also explain how they can be extended to the case of a partially-connected WASN, which is assumed to be pruned to a tree topology. Finally, we discuss the advantages and disadvantages of the various algorithms.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimal distributed minimum-variance beamforming approaches for speech enhancement in wireless acoustic sensor networks

et al. 2015

View full text Add to dashboard Cite

show abstract

“…In [9], we have presented a set of experimental results illustrating the impact of sampling rate mismatches in batch BSS and adaptive AEC applications. The results reveal that adequate synchronization is a key for the performance of the speech enhancement algorithms.…”

Section: Issues Of Sampling Rate Skewmentioning

confidence: 99%

A study on recognizing distorted speech over local distributed transducer networks

Zhao

Shin

Robledo-Arnuncio

et al. 2009

2009 IEEE International Conference on Acoustics, Speech and Signal Processing

Self Cite

View full text Add to dashboard Cite

In a collaborative scenario, a multiplicity of portable devices may constitute a network of distributed microphones, without a clearly defined geometric configuration or synchronization that can be taken advantage of for traditional microphone array processing to enhance the acquired signal. This application scenario represents a severe, but interesting challenge for automatic speech recognition systems. In this paper, we investigate a variety of robust speech recognition techniques with a focus on the distributed transducer scenario. We also report some important study results that lead to new thinking in the design of robust speech recognition for broadened applications. Two issues that are inherent to distributed transducer networks are specially investigated. First, we study the effect of the sampling rate skew of microphones to the system performance; second, we explore the possibility of combining recognition hypotheses from multiple transducer channels for improved recognition accuracy.

show abstract

“…However, the asynchronous channels brings many additional issues which are not treated conventionally in microphone array signal processing. For example, the array geometry is naturally unknown [2,3,4,5], the recording devices have different unknown gains [6], each device starts recording independently [4,5], and the sampling frequencies are not common among the observation channels [1,7,8,9].…”

Section: Introductionmentioning

confidence: 99%

Blind compensation of inter-channel sampling frequency mismatch with maximum likelihood estimation in STFT domain

Miyabe

Ono

Makino

2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

View full text Add to dashboard Cite

This paper proposes a novel blind compensation of sampling frequency mismatch for asynchronous microphone array. Digital signals simultaneously observed by different recording devices have drift of the time differences between the observation channels because of the sampling frequency mismatch among the devices. Based on the model that such the time difference is constant within each time frame, but varies proportional to the time frame index, the effect of the sampling frequency mismatch can be compensated in the short-time Fourier transform domain by the linear phase shift. By assuming the sources are motionless and stationary, a likelihood of the sampling frequency mismatch is formulated. The maximum likelihood estimation is obtained effectively by a golden section search.

show abstract

On Dealing with Sampling Rate Mismatches in Blind Source Separation and Acoustic Echo Cancellation

Cited by 33 publications

References 7 publications

Optimal distributed minimum-variance beamforming approaches for speech enhancement in wireless acoustic sensor networks

Optimal distributed minimum-variance beamforming approaches for speech enhancement in wireless acoustic sensor networks

A study on recognizing distorted speech over local distributed transducer networks

Blind compensation of inter-channel sampling frequency mismatch with maximum likelihood estimation in STFT domain

Contact Info

Product

Resources

About