A Review of Stimulating Strategies for Cochlear Implants

Choi, C.T.M.; Lee, Yi‐Hsuan

doi:10.5772/47120

Cited by 8 publications

(7 citation statements)

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“…Böylece HiRes 120 stratejisinde 2 komşu elektrot tarafından bir kanal oluşturulur. 63 Standart HiRes işlemlemede, gelen ses 16 spektral bant hâlinde analiz edilmektedir. Her bandın enerjisi çıkarılır ve zarf ile tek tek karşılık gelen elektrotlara saniyede 83.000 darbe maksimum uyarım hızında modülasyon yapar.…”

Section: Med-el Koklear İmplant Si̇stemleri̇nde Uyaran Hizinin Karşilaştirilmasiunclassified

“…Genel olarak bu çalışma, hem HiRes hem de HiRes 120'nin bireylerin dinleme ihtiyaçlarına göre uyarlanabilen etkili ses işlemleme stratejisi seçenekleri olduğunu göstermiştir. 63 AB tarafından yürütülen bir klinik çalışmada katılımcılarının sessiz ve gürültülü ortamlarda kelime tanıma ve cümle tanıma skorlarının, geleneksel HiRes ve HiRes Fidelity 120 arasında benzer olduğunu göstermiştir. 67 Donaldson ve ark., erişkin kullanıcılarda HiRes ve HiRes Fidelity 120 işlemleme stratejilerini inceledikleri çalışmalarında, konuşmayı ayırt etme skorlarında 2 stratejide belirgin bir fark görülmemekle birlikte, HiRes'in ünsüz algılamada üstünlüğünü bildirmişlerdir.…”

Section: Ab Koklear İmplant Si̇stemleri̇nde Kullanilan İşlemleme Strateji̇leri̇ni̇n Karşilaştirilmasiunclassified

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Signal Processing Strategies In Cochlear Implant Systems: A Review of Literature

Batuk¹,

Uzun²,

Koska³

et al. 2021

KBB ve BBC Dergisi

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Koklear implant (Kİ) teknolojileri hızlı bir şekilde gelişmektedir. Sinyal işlemleme ve konuşma kodlama stratejileri ise bu teknolojilerin gelişiminde başlıca ilgi duyulan konular arasında yer almaktadır. Gelişmeler yalnızca Kİ kullanıcılarının sessiz ve gürültülü ortamlarda konuşma algısını geliştirmeye çalışmakla kalmayıp, aynı zamanda müzik algısı ve tonal dillerde konuşma algısını geliştirmek için de ta-sarlanmaktadır. 1 Kİ elektrotlarının sınırlı uyarımı göz önünde bulundurularak, Kİ kullanıcılarına mümkün olan en net ve en doğal sesi sunmayı amaçlayan çeşitli sinyal işlemleme stratejileri geliştirilmiştir. 2 Konuşma işlemleme stratejileri 3 farklı firmaya ait stratejiler üzerinde incelenmiştir. Farklı firmalara ait sinyal işlemleme stratejileri ve bu stratejilerin genel özellikleri Tablo 1'de sunulmuştur.

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Section: Med-el Koklear İmplant Si̇stemleri̇nde Uyaran Hizinin Karşilaştirilmasiunclassified

Section: Ab Koklear İmplant Si̇stemleri̇nde Kullanilan İşlemleme Strateji̇leri̇ni̇n Karşilaştirilmasiunclassified

Signal Processing Strategies In Cochlear Implant Systems: A Review of Literature

Batuk¹,

Uzun²,

Koska³

et al. 2021

KBB ve BBC Dergisi

View full text Add to dashboard Cite

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“…The cochlear implant is a surgically implanted electronic device that restores partial hearing to a person who is deaf or hearing impaired [10][11][12]. This neural prosthesis provides similar functions of the inner ear by electrically stimulating the auditory nerve.…”

Section: Cochlear Implantsmentioning

confidence: 99%

Temporal Filterbanks in Cochlear Implant Hearing and Deep Learning Simulations

Lin¹

2017

Advances in Clinical Audiology

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The masking phenomenon has been used to investigate cochlear excitation patterns and has even motivated audio coding formats for compression and speech processing. For example, cochlear implants rely on masking estimates to filter incoming sound signals onto an array. Historically, the critical band theory has been the mainstay of psychoacoustic theory. However, masked threshold shifts in cochlear implant users show a discrepancy between the observed critical bandwidths, suggesting separate roles for place location and temporal firing patterns. In this chapter, we will compare discrimination tasks in the spectral domain (e.g., power spectrum models) and the temporal domain (e.g., temporal envelope) to introduce new concepts such as profile analysis, temporal critical bands, and transition bandwidths. These recent findings violate the fundamental assumptions of the critical band theory and could explain why the masking curves of cochlear implant users display spatial and temporal characteristics that are quite unlike that of acoustic stimulation. To provide further insight, we also describe a novel analytic tool based on deep neural networks. This deep learning system can simulate many aspects of the auditory system, and will be used to compute the efficiency of spectral filterbanks (referred to as "FBANK") and temporal filterbanks (referred to as "TBANK").

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“…However, their hearing ability degrades dramatically in noisy backgrounds ( 3 , 4 ). The main reason is that the signal processing in CIs is a very coarse imitation of the sound coding in a healthy cochlea ( 5 ). The inner hair cells (around 3,500), in charge of transforming sound vibrations in the cochlea into electrical signals, are replaced by only 12–26 implanted intracochlear electrodes.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Speech Enhancement With a Loss Trading Off the Speech Distortion and the Noise Residue for Cochlear Implants

2021

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The cochlea plays a key role in the transmission from acoustic vibration to neural stimulation upon which the brain perceives the sound. A cochlear implant (CI) is an auditory prosthesis to replace the damaged cochlear hair cells to achieve acoustic-to-neural conversion. However, the CI is a very coarse bionic imitation of the normal cochlea. The highly resolved time-frequency-intensity information transmitted by the normal cochlea, which is vital to high-quality auditory perception such as speech perception in challenging environments, cannot be guaranteed by CIs. Although CI recipients with state-of-the-art commercial CI devices achieve good speech perception in quiet backgrounds, they usually suffer from poor speech perception in noisy environments. Therefore, noise suppression or speech enhancement (SE) is one of the most important technologies for CI. In this study, we introduce recent progress in deep learning (DL), mostly neural networks (NN)-based SE front ends to CI, and discuss how the hearing properties of the CI recipients could be utilized to optimize the DL-based SE. In particular, different loss functions are introduced to supervise the NN training, and a set of objective and subjective experiments is presented. Results verify that the CI recipients are more sensitive to the residual noise than the SE-induced speech distortion, which has been common knowledge in CI research. Furthermore, speech reception threshold (SRT) in noise tests demonstrates that the intelligibility of the denoised speech can be significantly improved when the NN is trained with a loss function bias to more noise suppression than that with equal attention on noise residue and speech distortion.

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A Review of Stimulating Strategies for Cochlear Implants

Cited by 8 publications

References 8 publications

Signal Processing Strategies In Cochlear Implant Systems: A Review of Literature

Signal Processing Strategies In Cochlear Implant Systems: A Review of Literature

Temporal Filterbanks in Cochlear Implant Hearing and Deep Learning Simulations

Deep Learning-Based Speech Enhancement With a Loss Trading Off the Speech Distortion and the Noise Residue for Cochlear Implants

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