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Coding of Electrical Stimulation Patterns for Binaural Sound Coding Strategies for Cochlear Implants 41

Coding of Electrical Stimulation Patterns for Binaural Sound...

引用

41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Hinrichs, Reemt Gajecki, Tom Ostermann, Jorn Nogueira, Waldo Leibniz Univ Hannover Inst Informationsverarbeitung Appelstr 9a D-30167 Hannover Germany Hannover Med Sch Dept Otolaryngol Karl Wiechert Allee 3 D-30625 Hannover Germany

ISBN: (纸本)9781538613115

Binaural sound coding strategies can improve speech intelligibility for cochlear implant (CI) users. These require a signal transmission between two CIs. As power consumption needs to be kept low in CIs, efficient coding or bit-rate reduction of the signals is necessary. In this work, it is proposed to code the electrical signals or excitation patterns (EP) of the CI instead of the audio signals captured by the microphones. For this purpose we designed a differential pulse code modulation based codec with zero algorithmic delay to code the EP of the advanced combination encoder (ACE) sound coding strategy for CIs. Our EP codec was compared to the G.722 64 kbit/s audio codec using the signal-to-noise ratio (SNR) as objective measure of quality. On two audio-sets the mean SNR was 0.5 to 13.9 dB higher when coding the EP with the proposed coding method while achieving a mean bit-rate between 34.1 and 40.3 kbit/s.

关键词： cochlear implants excitation patterns advanced combination encoder differential pulse code modulation binaural sound coding strategy

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REAL-TIME IMPLEMENTATION OF WAVELET-BASED advanced combination encoder ON PDA PLATFORMS FOR COCHLEAR IMPLANT STUDIES

REAL-TIME IMPLEMENTATION OF WAVELET-BASED ADVANCED COMBINATI...

引用

2010 IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Gopalakrishna, V. Kehtarnavaz, N. Loizou, P. Univ Texas Dallas Dept Elect Engn Richardson TX 75080 USA

ISBN: (纸本)9781424442966

This paper presents the real-time implementation of wavelet-based advanced combination encoder on PDA platforms for cochlear implant studies. Three real-time implementations using the conventional FFT, our previous recursive DFT, and the wavelet transform are compared in terms of computational complexity, processing time and fixed-point accuracy. The results obtained show that this new real-time implementation on PDA platforms is computationally comparable to our previous real-time recursive DFT implementation while achieving a higher accuracy or a lower quantization error.

关键词： Cochlear implant signal processing real-time signal processing wavelet packet transform PDA platforms advanced combination encoder

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REAL-TIME IMPLEMENTATION OF WAVELET-BASED advanced combination encoder ON PDA PLATFORMS FOR COCHLEAR IMPLANT STUDIES

REAL-TIME IMPLEMENTATION OF WAVELET-BASED ADVANCED COMBINATI...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： V. Gopalakrishna N. Kehtarnavaz P. Loizou Department of Electrical Engineering University of Texas at Dallas Richardson TX 75080 USA

ISBN: (纸本)9781424442959

关键词： Cochlear implant signal processing real-time signal processing wavelet packet transform PDA platforms advanced combination encoder

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ROBUST AND EFFICIENT ENVIRONMENT DETECTION FOR ADAPTIVE SPEECH ENHANCEMENT IN COCHLEAR IMPLANTS

ROBUST AND EFFICIENT ENVIRONMENT DETECTION FOR ADAPTIVE SPEE...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Hazrati, Oldooz Sadjadi, Seyed Omid Hansen, John H. L. Univ Texas Dallas CRSS Richardson TX 75080 USA

ISBN: (纸本)9781479928934

Cochlear implant (CI) recipients require alternative signal processing for speech enhancement, since the quantities needed for intelligibility and quality improvement differ significantly when direct stimulation of the basilar membrane is employed for CIs. Here, a robust feature vector is proposed for environment classification in CI devices. The feature vector is directly computed from the output of the advanced combination encoder (ACE), which is a sound coding strategy commonly used in CIs. Performance of the proposed feature vector is evaluated in the context of environment classification tasks under anechoic quiet, noisy, reverberant, and noisy reverberant conditions. Speech material taken from the IEEE corpus are used to simulate different environmental acoustic conditions with: 1) three measured room impulse responses (RIR) with distinct reverberation times (T-60) for generating reverberant environments, and 2) car, train, white Gaussian, multi-talker babble, and speech-shaped noise (SSN) samples for creating noisy conditions at 4 different signal-to-noise ratio (SNR) levels. We investigate 3 different classifiers for environment detection, namely Gaussian mixture models (GMM), support vector machines (SVM), and neural networks (NN). Experimental results illustrate the effectiveness of the proposed features for environment classification.

关键词： advanced combination encoder cochlear implants environment detection noise reverberation

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