In-Vehicle Speech and Noise Corpora

Krishnamurthy, Nitish; Lubag, Rosarita; Hansen, John H. L.

doi:10.1007/978-1-4419-9607-7_9

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…We use "UT-Drive" corpora to perform the experiments in this study [34]. UT-Drive is a large-scale database of noise signals collected across different vehicle platforms under a wide range of field driving conditions.…”

Section: Datasetmentioning

confidence: 99%

Convolutional Neural Network-Based Speech Enhancement for Cochlear Implant Recipients

2019

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Attempts to develop speech enhancement algorithms with improved speech intelligibility for cochlear implant (CI) users have met with limited success. To improve speech enhancement methods for CI users, we propose to perform speech enhancement in a cochlear filter-bank feature space, a feature-set specifically designed for CI users based on CI auditory stimuli. We leverage a convolutional neural network (CNN) to extract both stationary and non-stationary components of environmental acoustics and speech. We propose three CNN architectures:(1) vanilla CNN that directly generates the enhanced signal;(2) spectral-subtraction-style CNN (SS-CNN) that first predicts noise and then generates the enhanced signal by subtracting noise from the noisy signal; (3) Wiener-style CNN (Wiener-CNN) that generates an optimal mask for suppressing noise. An important problem of the proposed networks is that they introduce considerable delays, which limits their real-time application for CI users. To address this, this study also considers causal variations of these networks. Our experiments show that the proposed networks (both causal and non-causal forms) achieve significant improvement over existing baseline systems. We also found that causal Wiener-CNN outperforms other networks, and leads to the best overall envelope coefficient measure (ECM). The proposed algorithms represent a viable option for implementation on the CCi-MOBILE research platform as a pre-processor for CI users in naturalistic environments.

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Section: Datasetmentioning

confidence: 99%

Convolutional Neural Network-Based Speech Enhancement for Cochlear Implant Recipients

2019

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“…A new vehicle noise corpus was established which consists of a range of conditions as described below to create the UTD-CAR-NOISE corpus and employed for analysis in this study (Krishnamurthy et al 2012). This corpus is the first of its kind, and offers unique in-vehicle research opportunities.…”

Section: Corpus Developmentmentioning

confidence: 99%

Car noise verification and applications

Krishnamurthy

Hansen

2013

Int J Speech Technol

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This study presents audio based vehicle-verification as a new area of research. The task involves verifying the claim that an acoustic sample belongs to a vehicle. Audio based vehicle verification has the potential to impact research in the areas of vehicle forensics and in-vehicle speech systems. For this task, a new corpus (UTD-CAR-NOISE) that consists of noise from 20 vehicles under 8 distinct noise environments (∼8 hours of data). Our approach towards vehicle verification hypothesizes that some specific environments are more suited for vehicle verification. Towards this goal, four diverse in-vehicle noise conditions are identified on the basis of their frequency of occurrence. Additionally, four different verification systems are proposed based on their complexity and modeling strategies. Our evaluation shows that A/C on with windows closed condition is the most conducive for vehicle verification (98 %). The proposed systems were evaluated on approximately 100,000 trials, achieving performances in the range of (75-98 %) for different vehicle environments.

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In-Vehicle Speech and Noise Corpora

Cited by 2 publications

References 5 publications

Convolutional Neural Network-Based Speech Enhancement for Cochlear Implant Recipients

Convolutional Neural Network-Based Speech Enhancement for Cochlear Implant Recipients

Car noise verification and applications

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