SearcharxivSearch

arXiv · 1506.03701

Channel Interaction and Current Level Affect Across-Electrode Integration of Interaural Time Differences in Bilateral Cochlear-Implant Listeners

Abstract

Sensitivity to ITDs is important for sound localization. Normal-hearing listeners benefit from across-frequency processing, as seen with improved ITD thresholds when consistent ITD cues are presented over a range of frequency channels compared to when ITD information is only presented in a single frequency channel. This study aimed to clarify whether cochlear-implant (CI) listeners can make use of similar processing when being stimulated with multiple interaural electrode pairs transmitting consistent ITD information. ITD thresholds for unmodulated, 100-pulse-per-second pulse trains were measured in seven bilateral CI listeners using research interfaces. Consistent ITDs were presented at either one or two electrode pairs at different current levels, allowing for comparisons at either constant level per component electrode or equal overall loudness. Different tonotopic distances between the pairs were tested in order to clarify the potential influence of channel interaction. Comparison of ITD thresholds between double pairs and the respective single pairs revealed systematic effects of tonotopic separation and current level. At constant levels, performance with double-pair stimulation improved compared to single-pair stimulation, but only for large tonotopic separation. Comparisons at equal overall loudness revealed no benefit from presenting ITD information at two electrode pairs for any tonotopic spacing. Irrespective of electrode-pair configuration, ITD sensitivity improved with increasing current level. Hence, the improved ITD sensitivity for double pairs found for a large tonotopic separation and constant current levels seems to be due to increased loudness. The overall data suggest that CI listeners can benefit from combining consistent ITD information across multiple electrodes, provided sufficient stimulus levels and that stimulating electrode pairs are widely spaced.

Explore related subjects

Keep this discovery

BibTeXRIS

Katharina Egger, Piotr Majdak, Bernhard Laback. 2015-06-11. Channel Interaction and Current Level Affect Across-Electrode Integration of Interaural Time Differences in Bilateral Cochlear-Implant Listeners. https://arxiv.org/abs/1506.03701

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Xiaomi-CocktailASR-1 Technical Report

Recently, large language model (LLM) based ASR models have achieved significant progress, yet they generally lack support for multi-speaker scenarios, where the cocktail party problem remains a critical bottleneck for further advancing ASR. Existing TS-ASR methods, including end-to-end architectures with speaker embeddings and latest LLM-based explorations suffer from degraded single-speaker performance and the inability to reject when the target speaker is absent. In this paper, we propose Xiaomi-CocktailASR-1, an LLM-based end-to-end TS-ASR architecture. By utilizing reference speech as voiceprint prompts, it directly transcribes the target speaker's speech without requiring speech separation. Xiaomi-CocktailASR-1 maintains competitive performance in single-speaker scenarios, comparable to mainstream ASR models. It also features a negative sample rejection capability, outputting empty text when the target speaker is absent from the mixed speech. Additionally, Xiaomi-CocktailASR-1 supports a Chain-of-Thought (CoT) reasoning mode to provide explicit reasoning steps. Extensive experiments on various synthetic and real-world multispeaker benchmarks demonstrate that Xiaomi-CocktailASR-1 achieves state-of-the-art performance, effectively addressing the cocktail party problem through a unified architecture that balances multispeaker and single-speaker recognition accuracy, along with rejection capability.

cs.SD

EConv-TasNet: Efficient Conv-TasNet for Effective Speech Separation

Conv-TasNet has served as a strong baseline for time-domain speech separation, and many studies have extended it with advanced architectures such as dual-path networks, U-Nets, and attention mechanisms. However, these methods often introduce high computational cost and complexity, limiting their deployment in resource-constrained scenarios. To address this issue, we propose eConv-TasNet, an efficient variant of Conv-TasNet that improves both effectiveness and efficiency without relying on resource-intensive modules. The proposed model consists of a group-wise early-splitting (GES) module and a multi-group feature aggregation (MGFA) module. GES generates discriminative speaker embeddings at intermediate stages, while MGFA progressively aggregates these group-level representations for refined mask estimation. Experimental results show that eConv-TasNet reduces model size by 22.4%, accelerates inference by 18.9%, and improves SI-SNRi by 14.0%-28.0% across three public benchmarks. Moreover, it achieves competitive performance compared with state-of-the-art methods while requiring significantly fewer parameters and lower inference cost. These results demonstrate a favorable efficiency-effectiveness trade-off for edge deployment.

cs.SD